Complete Guide to Text-to-Speech (TTS) Technology (2025)

TLDR:

Text-to-speech (TTS) has evolved from robotic computer voices to natural, human-like speech that powers Siri, Alexa, ChatGPT voice mode, and AI agents. This comprehensive guide explains how TTS works, compares leading solutions, and shows developers how to build voice-enabled applications with minimal latency.

What you'll learn:

• How modern neural TTS generates natural-sounding speech
• Why on-device TTS outperforms cloud APIs for conversational AI
• The critical difference between traditional, output streaming, and dual-streaming TTS
• How to evaluate vendor benchmark claims (and what they're hiding)
• Step-by-step implementation of on-device dual-streaming TTS for low-latency applications

Key takeaways:

Traditional TTS: Use for audiobooks, pre-scripted content, and non-interactive applications. Requires complete text and prepares complete audio before playback.

Response time when used in voice assistants: ~3+ seconds

Output Streaming TTS: Use for traditional NLP-powered real-time applications. Requires complete text but streams audio chunks.

Response time when used in voice assistants: ~1.5+ seconds

Dual-Streaming TTS: Use for conversational AI apps, LLM voice assistants, and real-time interactions. Processes incremental tokens as LLMs generate them and streams audio back in chunks.

Response time when used in voice assistants: ~500+ milliseconds

Cloud TTS: Use for media and entertainment applications where voice variety and quality matter more than speed.

Trade-off: Variable network latency (1000-2000ms+ total)

On-Device TTS: Use for real-time (latency-sensitive) and mission-critical (e.g., healthcare) applications where connectivity significantly impacts the experience.

Advantage: Guaranteed response time (180-350ms)

TTS engines are not created equally. Some on-device TTS may have slower response times than cloud TTS, and some on-device TTS may have more voice models than cloud alternatives. Let's dive into the details to learn the nuances.

Table of Contents

• What is Text-to-Speech?
• How Does Text-to-Speech Work?
• Text-to-Speech Training Approaches
• Text-to-Speech vs Other Technologies
• On-Device vs. Cloud Text-to-Speech
• Streaming vs Traditional Text-to-Speech
• Comparing Text-to-Speech Engines
• TTS Implementation Guide
• Platform-Specific Tutorials
• TTS Use Cases and Applications
• TTS Best Practices
• Getting Started with Orca Streaming TTS
• Additional Resources
• Conclusion
• Frequently Asked Questions

What is Text-to-Speech?

Text-to-speech (TTS), also called speech synthesis, is technology that converts written text into spoken audio. TTS analyzes text input, processes linguistic information, and generates audio output that mimics human speech patterns, intonation, and pronunciation.

TTS systems consist of two primary components. The front-end handles text analysis and converts raw text into phonetic representations. It manages text normalization, pronunciation rules, and prosody. The back-end handles speech synthesis and converts those phonetic representations into actual audio waveforms.

Modern TTS engines use deep neural networks trained on hours of recorded speech to produce natural-sounding voices across multiple languages and speaking styles.

Why Text-to-Speech Matters

Text-to-Speech isn't just a feature. The Text-to-Speech market is projected to grow significantly as voice becomes the primary interface for AI interactions. From healthcare accessibility to autonomous vehicles, TTS has evolved from assistive technology into a critical infrastructure that enables hands-free, eyes-free computing across industries. Modern enterprises rely on TTS to improve efficiency, safety, scalability, and customer experience. Here's where it delivers the most impact:

• Voice AI Agents: TTS enables natural responses in AI assistants, mobile apps, cars, wearables, and smart home devices. When paired with ASR and LLMs, it creates fluid, human-like voice interactions.
• Hands-Free Productivity: Listening frees users from the screen. Emails, articles, notifications, and reports can be consumed while commuting, exercising, cooking, or multitasking. This improves workflow and reduces screen fatigue.
• Warehouse & Logistics Automation: TTS delivers real-time voice instructions for pick-to-voice, inventory checks, and routing updates. This keeps workers' hands free and reduces operational errors.
• Manufacturing & Field Service: TTS powers voice-guided inspections, maintenance steps, and safety alerts. It improves compliance and minimizes training time on the factory floor.
• Transportation & Automotive: TTS supports navigation instructions, safety announcements, and fleet alerts even offline. This enhances reliability in vehicles and transit systems.
• Retail & Self-Service Kiosks: TTS enables voice-enabled kiosks, checkout systems, and digital signage with multilingual support and low-latency interactions.

How Does Text-to-Speech Work?

Text-to-speech is fundamentally different from audio playback or simple voice recording. Rather than playing back pre-recorded audio files, a TTS engine dynamically generates speech from any text input. Advanced TTS systems can adapt pronunciation, prosody, and pacing based on linguistic context.

Understanding how TTS works helps explain why different systems produce different results and why some architectural choices lead to better user experiences than others.

TTS Pipeline

Modern TTS systems process text through multiple stages, each contributing to the final audio quality:

1. Text Normalization

Text normalization converts raw text into a speakable format by expanding abbreviations, numbers, and symbols, such as

• $100: one hundred dollars
• Dr. Smith: Doctor Smith
• 3:45 PM: three forty five P M

This stage handles the messy reality of written text: inconsistent formatting, domain-specific notation, and contextual ambiguity. For instance, "St." might mean "street" or "saint" depending on context.

2. Linguistic Analysis

Linguistic analysis identifies sentence boundaries, parts of speech, and grammatical structure to understand how text should be spoken. It

• identifies questions versus statements, affecting intonation,
• recognizes emphasis and importance, affecting stress patterns,
• detects sentence boundaries, affecting pausing,
• understands syntactic structure, affecting prosody.
• This stage determines that "I didn't say he stole the money" can have seven different meanings depending on which word is emphasized.

3. Phonetic Conversion (Grapheme-to-Phoneme)

Phonetic conversion translates text to phonetic representations that specify how each word should be pronounced, such as

• read is pronounced as /riːd/ in the present tense or /rɛd/ in the past tense
• live is pronounced as /lɪv/ as a verb or /laɪv/ as an adjective
• bow is pronounced as /boʊ/ when taking a bow or /baʊ/ when referring to a bow and arrow.

This stage handles English's notoriously irregular spelling-to-sound mappings, including homographs. Unlike languages with consistent phonetic spelling, English requires sophisticated models to predict pronunciation.

4. Prosody Generation

Prosody generation determines pitch, duration, and emphasis patterns for natural-sounding speech. Prosody is what makes speech sound human rather than robotic. Key elements include

• pitch contour: rising voices at the end of questions and lowering at the end of statements
• stress patterns: emphasizing the important words
• rhythm: natural timing variations between syllables
• pausing: appropriate silences at phrase and sentence boundaries
• speaking rate: faster vs slower speed variations for emphasis or clarity

Poor prosody is the main reason why early TTS systems sounded robotic. The words were correct, but these key elements, such as rhythm and intonation, were unnatural or missing.

5. Acoustic Synthesis

Acoustic synthesis generates actual audio waveforms from phonetic and prosodic information. This is where the "voice" is created. Modern approaches use neural networks trained on hours of recorded speech to produce natural-sounding audio that captures subtle characteristics of human voice production.

6. Audio Output

The final stage streams or outputs the synthesized speech through audio hardware or saves it to files. For interactive applications, this stage must minimize latency because every millisecond counts when users are waiting for responses.

This six-stage pipeline explains why not all TTS systems sound the same. High-quality systems excel at each stage - from handling the messy reality of abbreviations and numbers to generating natural prosody that makes speech sound human. Lower-quality systems may skip steps or use simplified approaches, resulting in robotic or unnatural-sounding output.

Text-to-Speech Training Approaches

The acoustic synthesis stage (turning phonemes into audio) can be accomplished through different methods, each with trade-offs.

Traditional Approaches to Text-to-Speech

1. Concatenative Synthesis

Concatenative synthesis stitches together recorded speech segments from a database. First, voice actors record audio files saying all possible sound combinations (diphones, triphones). The system uses these recordings to splice them together to form new words and sentences.

Concatenative TTS systems can sound very natural when segments match well. However, these systems require large databases and offer limited flexibility. End users may experience audible glitches at splice points.

2. Formant Synthesis

Formant synthesis uses acoustic models to generate speech without human recordings. Mathematical models simulate the human vocal tract, generating speech based on acoustic parameters. Formant synthesis models are generally tiny, very flexible, and intelligible at high speeds. However, they often sound robotic, difficult to achieve naturalness.

3. Diphone Synthesis

A diphone is a speech unit that runs from the middle of one phoneme to the middle of the next phoneme. Diphone synthesis uses prerecorded speech units to generate speech. During synthesis, the system concatenates diphones and applies signal-processing techniques to smooth the joins.

Diphone synthesis TTS is compact and produces consistent, intelligible speech. However, these systems often lack natural prosody and can sound somewhat robotic due to limited variation in the prerecorded units.

Modern Deep Learning Approaches to Text-to-Speech

1. Statistical Parametric Speech Synthesis (HMM-Based Synthesis)

HMM-based synthesis generates speech using statistical models trained on recorded speech data. Instead of stitching together waveforms, it predicts acoustic parameters such as pitch, spectral envelope, and duration based on Hidden Markov Models. These parameters are then fed into a vocoder to generate audio.

HMM TTS is compact, highly flexible, and capable of controlling prosody and voice characteristics. However, the resulting speech often sounds "buzzy" or muffled due to vocoder limitations and oversmoothing in the statistical models.

2. Two-staged Neural Text-to-Speech

This uses a widely accepted two-staged framework that divides the speech synthesis process into two distinct, separately trained neural network models: Acoustic Model (Text → Acoustic Feature) and Neural Vocoder (Acoustic Feature → Waveform). In the first stage, intermediate acoustic representations, most commonly mel-spectrograms, are generated from text (phonemes). The second stage takes these intermediate representations and converts them into the final, high-fidelity raw audio waveform that can be played as sound.

3. End-to-End Neural Text-to-Speech

End-to-End (E2E) Neural Text-to-Speech converts text directly into speech using deep learning models that jointly learn linguistic features, acoustic patterns, and prosody. (Text → Waveform). Since the models learn all dependencies jointly, they achieve highly natural, humanlike speech with expressive intonation and minimal artifacts. However, E2E may require more training data and may demand more computational resources.

Text-to-Speech vs Other Technologies

Understanding the differences between Text-to-Speech and related technologies helps you choose the right solution.

Text-to-Speech vs. Audio Playback

Text-to-speech dynamically generates speech from any text input in real-time. Audio playback, as the name suggests, plays pre-recorded audio files without text processing. Some developers use pre-recorded audio files instead of TTS. This approach fits certain use cases, such as announcements in public transportation about an upcoming station. However, audio playback has significant limitations in scenarios requiring flexibility because pre-recorded audio cannot generate dynamic or user-specific content. Large audio file libraries consume significant storage. Updates require re-recording and distributing new audio files.

Text-to-Speech vs. Voice Cloning

Text-to-speech and voice cloning are complementary technologies. Text-to-speech converts text to speech using pre-built voice models. Voice cloning creates custom TTS models that mimic specific individuals' voices.

Text-to-Speech vs. Speech Synthesis

These terms are essentially interchangeable. "Text-to-speech" emphasizes the input format (text), while "speech synthesis" emphasizes the output (synthesized speech). Both refer to the same technology.

Which is Better: On-Device or Cloud Text-to-Speech?

One of the most important architectural decisions when building voice applications is where the TTS engine runs. Cloud Text-to-Speech services like ElevenLabs, AWS Polly, and Google Text-to-Speech have gained popularity because they're easy to integrate and offer large voice libraries. However, they introduce fundamental trade-offs around latency, privacy, and reliability that become critical as your application scales or handles sensitive data. On-device TTS takes a different approach by running the synthesis engine entirely on the user's device.

On-Device Text-to-Speech Benefits

On-device TTS processes everything locally, offering several key advantages:

• Privacy: No audio or text data is sent to or processed in remote servers, ensuring privacy.
• Low-latency: Zero network round-trip delays enable instant responses. The system synthesizes speech without relying on internet connectivity, providing reliability.
• Scalability: There's no server infrastructure or bandwidth costs, enabling scalability. Data transmission overhead is eliminated, improving efficiency.

Cloud Text-to-Speech Benefits

Cloud TTS offers more flexibility to developers:

• Voice Variety: Access to larger libraries of voice models provides voice variety.
• Model Updates: Centralized model improvements happen without device updates.
• Computational Resources: Powerful cloud GPUs provide computational resources for high-quality, deep fake applications.

Hybrid Text-to-Speech Approaches

Some enterprises leverage both depending on their needs. They use on-device TTS for latency-critical tasks like real-time responses in voice assistants and cloud TTS for resource-intensive synthesis like high-quality voiceovers or audiobook production.

For most interactive applications, particularly AI assistants and voice interfaces, on-device TTS provides the best user experience by delivering cloud-quality voice generation entirely on-device with minimal latency, enabling natural interaction flow.

Streaming vs Traditional Text-to-Speech

The emergence of Large Language Models (LLMs) has created new requirements for Text-to-Speech. Traditional TTS engines cannot keep pace with how modern AI systems generate responses, creating awkward pauses that break conversational flow.

Traditional Text-to-Speech

Traditional, also known as single synthesis, text-to-speech requires complete text input before generating audio. It synthesizes the whole speech at once before playing. Therefore, the system waits for full input, then processes it into a complete audio file and plays it back. This creates a fundamental mismatch with how LLMs work.

Traditional TTS flow before the end users start hearing a response

• Waits for LLM to generate a complete response (2-5+ seconds)
• Receives the full text
• Begins processing the entire text
• Synthesizes the full audio using the entire text

This creates noticeable delays. Users ask a question, then wait in silence while the LLM composes a response and TTS processes it. The experience feels unnatural — like talking to someone who thinks for several seconds before each reply.

Why traditional TTS jeopardizes the user experience in real-time LLM applications

Imagine asking your voice assistant, such as "Hey Siri, what's the weather forecast for this week?" With traditional TTS:

• LLM takes 3 seconds to generate the response: "The weather this week will be cool, with highs ranging roughly 6 to 10°C and lows from 0 to 7°C. Expect some rain on Friday afternoon, Sunday morning, and possibly Wednesday. The best days for being outdoors look like Saturday and Monday."
• TTS receives the full text and begins processing
• TTS generates a 3-4 second-long audio file

The user experiences 5+ seconds of silence while the LLM generates the full response and TTS prepares the full audio before hearing anything.

Output Streaming Text-to-Speech

Output streaming TTS waits for the complete text as the input, but produces and plays audio in chunks. This is simply referred to as Streaming TTS by many TTS providers, such as Amazon Polly, Azure Text-to-Speech, and OpenAI.

Output Streaming TTS flow before the end users start hearing a response

• Waits for LLM to generate a complete response (2-5+ seconds)
• Receives the full text
• Starts synthesizing and playing the audio as it progresses

TTS continues to process new text chunks concurrently, and audio continues smoothly. This approach is sufficient for the traditional NLP engines that generate responses at once. However, outputs of LLMs are also streaming, akin to humans typing a response in real-time. Hence, similar to traditional TTS, output streaming TTS is an outdated approach in today's post-LLM era.

Why Output Streaming TTS jeopardizes the user experience in real-time LLM applications

• LLM takes 3 seconds to generate the response: "The weather this week will be cool, with highs ranging roughly 6 to 10°C and lows from 0 to 7°C. Expect some rain on Friday afternoon, Sunday morning, and possibly Wednesday. The best days for being outdoors look like Saturday and Monday."
• TTS speaks these words immediately after receiving the full LLM response

The user experiences 3+ seconds of silence while the LLM generates the full response, and output streaming TTS generates the first chunk of the audio before hearing anything.

Dual Streaming Text-to-Speech

Dual-streaming TTS represents the next generation of speech synthesis technology. Unlike traditional TTS, which waits for complete sentences, or output-streaming TTS, which waits for complete input, dual-streaming processes both input and output incrementally. Think of it as the difference between reading an entire book before discussing it versus having a conversation about it as you read it together.

Dual-stream TTS receives text input fed in chunks, often word-by-word or even character-by-character, and returns audio chunks as soon as there is enough context to produce natural speech. Picovoice's Orca Streaming Text-to-Speech is an example of a dual-streaming TTS engine. We also refer to this type of streaming as the PADRI engine.

Dual Streaming TTS flow before the end users start hearing a response

• Waits for LLM to generate first tokens (100-300ms)
• Begins synthesis immediately with available text and playing when there is sufficient text to produce natural speech (100-200 ms)

Dual Streaming TTS processes new text chunks concurrently, as LLMs continue generating additional tokens and audio continues smoothly. TTS completes speech synthesis shortly after LLM finishes.

Why Is Dual-Streaming TTS Essential for LLM Applications?

• LLM takes 300-400 ms to generate first tokens: "The weather..."
• TTS speaks these words immediately in 100-200 ms, while LLM composes the rest
• LLM and TTS continue to generate the response as the end users listen

Although processing input as LLMs generate responses gives Cloud Streaming Text-to-Speech a head start, due to the large size of audio data (text data is much smaller than audio data), users still experience more than a second-long-delay in total.

Efficient, on-device dual streaming TTS, such as Orca, takes cloud-based dual streaming TTS one step further and eliminates the network latency, start reading before the cloud-based dual streaming starts.

Tutorials below demonstrate how streaming TTS eliminates awkward pauses and creates natural conversation flow with LLMs:

• Add Voice to Claude
• Add Voice to ChatGPT
• Add Voice to Perplexity
• Add Voice to DeepSeek
• Add Voice to Mistral

Using on-device LLM can eliminate the network latency prior to LLM, resulting in even lower latency for voice AI agents. Tutorials below demonstrate how streaming TTS eliminates awkward pauses and creates natural conversation flow with on-device LLMs:

• Fully on-device Android voice assistant
• Fully on-device iOS voice assistant
• Fully on-device web-based voice assistant
• Fully on-device Python voice assistant

Advanced Streaming Text-to-Speech Capabilities

• Token-by-Token Processing, aka Dual Streaming TTS: Advanced Streaming TTS engines synthesize speech from incremental text chunks as small as individual words or phrases.
• Partial Sentence Handling: Advanced Streaming TTS engines adjust prosody as context emerges, despite starting to speak before seeing the complete sentence.
• Dynamic Context Awareness: Advanced Streaming TTS engines adapt intonation and emphasis based on emerging sentence structure.
• Graceful Handling: Advanced Streaming TTS processes punctuation, sentence boundaries, and formatting on-the-fly

How Do I Choose the Right TTS Engine for My Application?

TTS systems are evaluated on multiple dimensions that together determine the user experience, including quality (naturalness, intelligibility) and latency.

Naturalness

Naturalness measures how closely synthesized speech resembles human speech. Key factors include:

• prosody (natural rhythm, stress, and intonation patterns)
• emotion (appropriate expressiveness and tone)
• voice quality (smoothness without artifacts or robotic qualities)
• pronunciation (accurate word and phoneme production)

Assessment methods include Mean Opinion Score (MOS), where listeners rate naturalness on a 1 to 5 scale. Minimizing bias and achieving more reliable results require tests with more listeners (test subjects) from diverse backgrounds, taking time and effort. Comparative benchmarks also use A/B testing against human speech or competitor TTS. Again, in order to minimize the bias, a wide variety of participants is required.

If vendors report naturalness using MOS or A/B tests without disclosing how they selected the participants, question the reliability of the results, as they may simply ask their employees to evaluate alternatives.

Intelligibility

Intelligibility measures how easily listeners understand synthesized speech. Factors include:

• clarity (distinct pronunciation of phonemes and words)
• articulation (clear consonants and vowel sounds)
• speech rate (appropriate speed for comprehension)
• consistency (reliable pronunciation across contexts)

Assessment methods include word error rate, which measures transcription accuracy when listeners write down what they hear. Comprehension tests measure listener understanding of content.

Intelligibility is a metric that can be manipulated easily. If vendors report intelligibility without disclosing their methodology and participants, unless vendors use automated systems, question the reliability of the results. Again, they may lead the participants in favor of their solutions.

Latency

Latency can be defined differently by vendors. It's mostly used to measure the time between text input and audio output. Latency is critical for interactive applications like voice assistants and conversational AI, or real-time translation for live speech-to-speech systems.

Latency has several components. Processing delay is the time to analyze text and generate audio. First audio latency is the time until the first audio output (critical for streaming TTS). Real-time factor (RTF) is the ratio of synthesis time to audio duration. An RTF less than 1 means faster than real-time.

Understanding TTS Latency Benchmarks

When evaluating TTS latency claims, it's essential to understand what's actually being measured and what affects real-world user experience.

Cloud TTS vendors may advertise impressive "model latency" numbers. For example, ElevenLabs' documentation promotes 75ms latency for Flash with an asterisk acknowledging that ElevenLabs doesn't include application and network latency. Network round-trip time accounts for data transmission to and from cloud servers, which varies by location, connection quality, and congestion. Application processing accounts for the time that the application needs to prepare and send requests. Audio buffering creates a delay before audio playback begins. Infrastructure overhead includes load balancing, authentication, and request queuing.

ElevenLabs' latency increases to 135 ms in another document when the first byte audio latency is measured. The 60 millisecond gap represents additional overhead, but even this doesn't account for everything. Variable network conditions mean network latency is not guaranteed and varies by user location, connection quality, and network congestion. Unquantified components like "application latency" get mentioned but not measured in benchmarks. Ideal conditions mean benchmarks often assume optimal server proximity and network conditions.

To put this in perspective: a blink of your eye takes 100-150 milliseconds. When vendors advertise 75ms model latency, but the actual user experience is much higher than 135 ms, because even 135 ms doesn't include all the factors affecting TTS latency.

When the time is taken from the moment the LLM produces the first text token to the TTS engine produces the first byte of speech, i.e., First Token to Speech (FTTS) is measured the open-source benchmark shows that Orca starts reading 6.5x faster than ElevenLabs, thanks to its on-device processing capabilities.

TTS Latency Impact on User Experience:

End users don't distinguish between "model latency," "network latency," or "application latency." They only experience total delay. A conversation that feels unnatural due to network delays creates the same poor experience as slow processing.

Cloud vs On-Device TTS Latency

Cloud TTS (with network round-trip): Text data is sent to the cloud for speech synthesis, and audio is sent back to the end users' devices. The main problem with the network latency is that it's variable and unpredictable based on connection quality.

On-device TTS (no network dependency): Speech is synthesized directly on the user's device - eliminating network dependencies entirely. The latency depends on the device's capability. It offers a guaranteed (consistent and predictable) response time regardless of connectivity.

For Conversational AI:

In voice assistant applications using LLMs, the latency picture becomes more complex. Most vendors, such as Amazon, Google, OpenAI, Deepgram recommend cloud STT, LLM, and Output Streaming TTS for conversational AI applications, which come with significant drawbacks.

1. User speaks → ASR (network latency)
2. ASR sends text data to the LLM (network latency)
3. LLM processes query (network latency)
4. LLM completes response generation and sends the full text to TTS (network latency)
5. TTS starts speech synthesis (network latency)
6. Audio is sent to the user's device (network latency)
7. User hears response

Streaming On-Device STT, LLM, and Dual-Streaming TTS

1. User speaks → ASR
2. LLM begins generating a response
3. First tokens → TTS processes immediately
4. Audio begins playing while LLM continues generating
5. User hears the response with minimal delay

Cloud architectures don't just add latency once. They multiply it. There are 4 or more network round-trips for a complete voice interaction. Each round-trip varies based on user and server location, connection quality, and server load. Network delays compound with processing delays. Unpredictable variance creates an inconsistent user experience.

On-device architectures minimize multiplication. There are zero (or minimal when deployed in a closed network server) network dependencies for the whole system. Processing time stays consistent regardless of connectivity. Users get a predictable, low-latency experience every time.

This architectural difference explains why cloud vendors often quote only "model latency." The complete picture reveals significantly higher real-world delays that on-device solutions avoid entirely.

Benchmark Transparency

When evaluating TTS benchmarks, demand complete end-to-end measurements including all real-world components. Network conditions should be specified (ideal versus typical versus poor connectivity). Geographic testing should happen from multiple locations relative to servers. Real application scenarios, not isolated components, should be tested.

Picovoice provides transparent benchmarks measuring actual latency in realistic conditions. See the Open-source TTS Latency Benchmark for a detailed comparison among popular vendors, or reproduce the results for each vendor:

• ElevenLabs TTS Latency
• OpenAI TTS Latency
• Azure TTS Latency
• Amazon Polly Latency

Efficiency

Efficiency measures computational resource utilization. This includes CPU usage (processing power required for synthesis), memory footprint (RAM requirements), model size (storage space for TTS models), and battery impact (power consumption on mobile devices). Efficiency is not a metric that can be used to evaluate cloud TTS APIs, as developers do not have access to vendors' models.

TTS Benchmark Methodology Best Practices

Test Data Selection

Use diverse, representative test data that consists of:

• Multiple text domains (news, dialogue, technical, narrative)
• Various sentence lengths and complexities
• Different speaking contexts (informative, conversational, expressive)
• Real-world content, not artificial test sentences

Platform Consistency

Test TTS systems on actual target hardware:

• Consumer-grade devices, not high-end development machines
• Multiple device generations and specifications
• Various operating system versions
• Real network conditions (not isolated lab environments)

Statistical Validity

Ensure TTS engines generate reliable results:

• Run multiple tests to account for variance
• Have sufficient sample sizes for meaningful averages
• Analyze and investigate outliers

Transparency Requirements

Demand TTS vendors provide reproducible benchmarks with:

• Complete methodology documentation
• Test data availability (or detailed description)
• Hardware/software specifications
• Step-by-step reproduction instructions
• Raw results, not just summary statistics

Common TTS Benchmark Pitfalls

Cherry-Picking Metrics

• Vendors may highlight favorable metrics while omitting unfavorable ones:
• Advertising "model latency" while excluding "other" delays
• Reporting best-case performance as typical results
• Measuring quality on carefully curated test sets
• Testing on unrealistic hardware configurations

Inconsistent Conditions

• Fair comparisons require identical testing conditions
• Testing own cloud TTS from data center vs. others on target devices
• Comparing pre-warmed models vs cold-start scenarios
• Using optimal network conditions vs realistic connectivity
• Testing with prepared text vs dynamic generation

Narrow Test Coverage

• Limited TTS testing misses real-world challenges:
• Testing only simple, well-formed sentences
• Using only news articles or prepared scripts
• Ignoring edge cases and error conditions

Misleading Presentation

• TTS benchmark results can be presented to obscure weaknesses:
• Selective time windows or conditions
• Aggregating dissimilar metrics
• Comparing different aspects as if equivalent

Evaluating TTS Vendors' Benchmark Claims

When reviewing TTS benchmark claims, apply critical analysis:

Question Incomplete Metrics

When a vendor claims "75ms latency," ask:

• How is the latency defined?
• Does this include network transmission time?
• What network conditions are assumed?
• What percentile is reported (median, 95th, 99th)?
• Can this be reproduced independently?

Demand Real-World Testing

Benchmark conditions should match actual usage:

• Geographic diversity (not just nearest data center)
• Network variability (not just ideal conditions)
• Device specifications (not just latest hardware)
• Concurrent load (not just single-user testing)

Picovoice Open-source TTS Latency Benchmark

Picovoice provides a transparent, reproducible, open-source benchmark framework for TTS latency with publicly available testing methodology and data. Picovoice's TTS latency benchmark framework provides complete latency measurement with end-to-end timing, including all overhead. See the Open-source TTS Latency Benchmark for detailed performance data, methodology, and reproduction instructions.

TTS Implementation Guide

Ready to add Text-to-Speech to your application? Here's how to get started.

Step 1: Choose Your TTS Engine

Commercial Options:

• Orca Streaming Text-to-Speech - On-device TTS engine optimized for LLMs with advanced dual-streaming capabilities
• Amazon Polly - Cloud-based TTS with multiple voices
• Google Cloud Text-to-Speech - Cloud service with WaveNet voices
• Microsoft Azure Speech - Cloud TTS with neural voice options
• ElevenLabs - High-quality cloud TTS with voice cloning

Open Source Options:

• Coqui TTS - Open-source neural TTS with multiple architectures
• piper - Fast, lightweight TTS for edge devices
• Festival - Legacy academic TTS system
• eSpeak - Compact formant-based synthesis

Recommendation: For interactive real-time applications, AI assistants, and LLM integration, Orca Streaming Text-to-Speech offers the best combination of quality, latency, and privacy.

Step 2: Select Your Voice

Consider your application requirements. Language support should match your target languages. Gender selection should be appropriate for your use case. Style should match voice personality to your brand. Customization needs should be determined based on your requirements.

Orca Streaming Text-to-Speech provides default male and female voices optimized for natural conversation. Enterprise customers can create fully custom voices.

Step 3: Integrate into Your Application

Orca provides SDKs for every major platform.

Mobile platforms include iOS, Android, React Native, and Flutter. Web platforms include JavaScript and React. Desktop platforms include Windows, macOS, and Linux. Embedded platforms include Raspberry Pi and microcontrollers. Programming languages include Python, Node.js, Java, C, .NET, and Go.

Basic TTS Implementation in Python

import pvorca

orca = pvorca.create(access_key='${ACCESS_KEY}')
pcm, alignments = orca.synthesize(text='${TEXT}')

Streaming TTS Implementation in Python (for LLM integration)

import pvorca

orca = pvorca.create(access_key='${ACCESS_KEY}')
stream = orca.stream_open()

# Thread 1: LLM generates tokens and fills a buffer
tokens = get_llm_output()

# Thread 2: audio loop consumes tokens and produces PCM
for token in tokens:
    pcm = stream.synthesize(token)
    if pcm is not None:
        # handle pcm
        pass

Step 4: Optimize and Test

• Test with diverse text samples: Test with diverse text samples, including technical terms, numbers, and punctuation.
• Evaluate across target devices: Measure performance on actual hardware.
• Adjust synthesis parameters: Adjust synthesis parameters to tune speech rate and voice characteristics.
• Monitor resource usage: Track CPU, memory, and battery impact of TTS in real-world environments.
• Gather user feedback: Collect subjective quality assessments from actual users.

Platform-Specific Tutorials

Python is just one of the platforms supported by Orca Streaming Text-to-Speech. Choose your platform to get started with Text-to-Speech:

TTS for Web Applications

• Streaming Text-to-Speech in JavaScript
• Local LLM-Powered Voice Assistant for Web Browsers

TTS for Desktop Applications

• Streaming Text-to-Speech in .NET
• Streaming Text-to-Speech in Python

TTS for Mobile Applications

• AI Voice Assistant for iOS Powered by Local LLM
• AI Voice Assistant for Android Powered by Local LLM

Embedded & IoT

• Text-to-Speech on Raspberry Pi

Multilingual TTS

• Spanish Text-to-Spanish

TTS Use Cases and Applications

Text-to-speech enables voice experiences across countless applications.

1. AI Assistants & Conversational AI

Voice-enabled AI assistants respond naturally to user queries. Streaming TTS enables fluid conversations without robotic pauses between responses.

Build your own AI voice assistant, leveraging Picovoice on-device Voice AI stack:

• On-Device LLM-Powered Voice Assistant
• Add Voice to Claude
• Add Voice to ChatGPT
• Add Voice to Perplexity

2. Accessibility

Screen readers, content readers, and assistive technologies help visually impaired users. TTS converts written content into accessible audio.

3. Enterprise & Productivity

Hands-free information access, document narration, and workflow automation all benefit from TTS. It increases productivity by enabling multitasking.

4. Consumer Electronics

Voice-enabled devices and appliances, smart home systems, and automotive infotainment all rely on TTS. It enables voice feedback and instructions.

5. Retail & Hospitality

Vending machines, kiosks, and customer service applications use TTS. It enhances customer interactions.

TTS Best Practices

Building high-quality TTS experiences requires attention to multiple factors beyond just speech synthesis quality.

Privacy

• Use on-device TTS when possible to protect user data
• Minimize cloud dependencies for sensitive content
• Be transparent about data handling practices

User Experience

• Provide audio controls - Allow end users to adjust volume, speech rate, pause/resume
• Visual feedback - Show end users the text being read
• Contextual appropriateness - Match voice style to application tone
• Inform users - Let end users know they're interacting with AI

Testing, Monitoring & Performance Optimization

• Test diverse content - Various text types, lengths, complexity
• Measure real-world latency - Include all delays (network, compute, etc.) and keep it minimal for interactive, real-time applications
• Manage memory efficiently for resource-constrained devices
• Leverage batch (non-streaming) processing for non-real-time synthesis
• Monitor resource usage - CPU, memory, battery consumption
• Collect user feedback - Subjective quality assessments
• Monitor error rates - Track synthesis failures and degraded output

Voice Selection

• Match voice to audience - Age, gender, cultural considerations
• Align with brand - Personality and tone
• Test with users - Gather feedback on voice preferences
• Consider multilingual needs - Support for multiple languages
• Accessibility - Ensure clarity for diverse listener abilities

Cross-Platform Development

• Use consistent SDKs across platforms when possible
• Maintain voice consistency across devices
• Handle platform differences gracefully
• Test offline scenarios thoroughly
• Plan for model updates and distribution

Getting Started with Orca Streaming TTS

For interactive real-time LLM applications, AI assistants, and agents, Orca Streaming Text-to-Speech offers the best combination of quality, latency, and privacy.

Orca Streaming Text-to-Speech Benefits

Orca Streaming Text-to-Speech is an advanced dual-streaming TTS engine, optimized for LLM applications. Orca offers:

• Natural-sounding voices
• Lowest latency in the market
• Complete on-device processing ensures privacy
• Cross-platform support covers mobile, web, desktop, and embedded devices
• Flexible speech control parameters (speech rate, etc.) adapt to your needs
• Advanced phonetic conversion, including homographs, and prosody generation
• Free plan for personal projects and free trial for commercial projects

Streaming Text-to-Speech Documentation

• Orca Streaming Text-to-Speech Python Quick Start
• Orca Streaming Text-to-Speech Python API
• Orca Streaming Text-to-Speech C Quick Start
• Orca Streaming Text-to-Speech C API
• Orca Streaming Text-to-Speech .NET Quick Start
• Orca Streaming Text-to-Speech .NET API
• Orca Streaming Text-to-Speech Node.js Quick Start
• Orca Streaming Text-to-Speech Node.js API
• Orca Streaming Text-to-Speech Android Quick Start
• Orca Streaming Text-to-Speech Android API
• Orca Streaming Text-to-Speech iOS Quick Start
• Orca Streaming Text-to-Speech iOS API
• Orca Streaming Text-to-Speech Web Quick Start
• Orca Streaming Text-to-Speech Web API

Additional Resources

Technical Deep Dives

• TTS APIs and SDKs
• Text-to-Speech Applications
• Orca: True Streaming TTS
• Streaming Text-to-Speech for LLMs
• Streaming TTS for AI Agents
• Real-Time Text-to-Speech
• Local Text-to-Speech with Cloud Quality

Comparison & Strategy

• Choosing the Best Text-to-Speech
• Cloud Text-to-Speech
• Understanding Text-to-Speech Latency

LLM Voice Integration

• Add Voice to Claude
• Add Voice to ChatGPT
• Add Voice to Perplexity
• On-Device LLM-Powered Voice Assistant
• Local LLM-Powered Voice Assistant for Web Browsers
• AI Voice Assistant for iOS with Local LLM
• AI Voice Assistant for Android with Local LLM

Conclusion

Text-to-speech technology has transformed from robotic computer voices to natural, human-like speech that powers modern voice experiences. The convergence of neural TTS models, on-device processing, and streaming architectures has made it possible to deliver conversational AI that feels genuinely responsive and natural.

The emergence of Large Language Models has elevated TTS from a "nice-to-have" feature to a critical component of user experience. Users no longer tolerate robotic voices or awkward delays. They expect instant, natural responses that flow conversationally. Meeting these expectations requires understanding not just TTS technology, but the architectural decisions that determine real-world performance.

Key Takeaways

Text-to-Speech (TTS), also known as speech synthesis, converts written text to spoken audio.

• Deep Learning Powered vs. Traditional (Legacy) TTS: Deep learning powered TTS produces natural-sounding spoken audio using neural networks trained on human speech, whereas traditional (legacy) TTS produces robotic audio.
• On-device vs. Cloud TTS: On-device offers superior privacy, latency, and reliability compared to cloud solutions by eliminating inherent cloud limitations.
• Dual Streaming vs. Output Streaming TTS: Dual-streaming TTS enables fluid AI conversations by processing text incrementally as LLMs generate responses, avoiding awkward pauses.
• Voice Cloning vs. Text-to-Speech: Complementary technologies. Voice cloning creates custom TTS models that mimic specific individuals' voices. TTS engines use these models to create audio.
• TTS Benchmarks: TTS benchmarks may include various metrics, including naturalness, intelligibility, latency, and efficiency.

Various factors affect the TTS Benchmark results. Demand complete disclosure of the test data and test methodology. Learn more about Text-to-Speech Latency Benchmarks.

The Path Forward

As AI becomes more conversational, voice interfaces will become the primary way users interact with technology. Text-to-Speech sits at the critical junction between AI intelligence and human experience. It's the last step that determines whether an interaction feels natural or robotic.

Choosing the right TTS solution requires looking beyond marketing claims to understand architectural fundamentals. Does the solution work entirely on-device or require cloud connectivity? Can it process streaming text from LLMs or only complete sentences? What's the true end-to-end latency in real-world conditions? How does it perform on your target hardware and network conditions? Can you reproduce benchmark claims independently?

For developers building conversational AI, voice assistants, or any application where latency and naturalness matter, on-device streaming TTS provides the foundation for exceptional user experiences.

Start Building

Whether you're building an AI assistant, accessibility tool, content platform, or voice-enabled application, Orca Streaming TTS provides the foundation for fast and natural voice experiences.

Frequently Asked Questions