Recognize, verify, and identify speakers in real time
Enterprise developers' no. 1 choice. Cross-platform, text-independent, language-agnostic speaker recognition with seamless enrollment.
Eagle Speaker Recognition is speaker verification and identification software that distinguishes individuals using their unique voice characteristics. Eagle Speaker Recognition, powered by deep learning, enables developers to determine "who is speaking" on any platform with no language or passphrase limitations.
Get started with just a few lines of code
Whether enterprises are personalizing smart devices or identifying speakers in meetings, Eagle Speaker Recognition gives developers real-time performance without compromise. Eagle Speaker Recognition is the only readily available enterprise-grade, highly accurate, resource-efficient, cross-platform, text-independent, and language-agnostic engine with a seamless enrollment process.
Eagle is engineered from the ground up for speed, accuracy, and low resource usage. It doesn't introduce compute latency while eliminating the network latency, making Eagle Speaker Recognition ideal for real-time applications.
Deployment in minutes, enrollment in seconds
Get to know users, personalize experiences, and build trust
Why choose Eagle Speaker Recognition over other Speaker Recognition Engines?
- Production-ready
- Cross-platform SDKs
- Text-independent
- Language-independent
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Frequently asked questions
Speaker Recognition deals with speaker identification and verification using distinguishable voice characteristics. It focuses on "who" rather than "what".
Speaker Identification, also known as Speaker Search or Speaker Spotting, is a specialized application of speaker recognition that determines the identity of an unknown speaker by comparing their voice characteristics with those of known speakers.
Speaker Verification, also known as Voice Biometrics, Voice Authentication, and Voiceprinting, is a subset of speaker recognition that focuses on verifying individuals' identities using unique voice patterns.
Speaker Identification and Speaker Verification are both subsets of Speaker Recognition. If a Speaker Recognition engine does a one-to-one match to verify the claimed identity, it's called Speaker Verification. If it does a one-to-many match, i.e., determines the speaker's identity within a group of enrolled speakers, it's called Speaker Identification.
Developers use Speaker Recognition and Identification in several applications. Some of them are:
- Meeting Speaker Identification: Automatically identifies and tracks speakers in conference calls, video meetings, and recorded conversations, making speaker recognition a perfect addition for meeting transcription services, CRM integration, and conversation analytics.
- Voice ID Authentication: Builds secure voice biometrics authentication into mobile apps, web applications, and IoT devices. Replace passwords with voice-based user verification.
- Audio Content Analysis: Processes podcasts, recordings, and media files to identify speakers, segment content, and enable speaker-based search and organization.
- Smart Home & IoT Devices: Enables personalized experiences by recognizing family members and authorized users through voice identification.
The best speaker recognition engine varies among enterprises, depending on their priorities and needs. Performance, Platform Support, Scalability, Compliance, Ease of Use, Developer-Friendliness, Availability of Support, and the Total Cost of Ownership are the most important factors to consider before a decision.
Yes. Picovoice researchers have published an open-source Speaker Recognition benchmark comparing Eagle Speaker Recognition with pyannote, and SpeechBrain. Azure AI Speaker Recognition is not included in the benchmark as it's not available for benchmarking purposes or public use in general. Azure AI Speaker Recognition is a retired product, and even before that, it was available to selected customers only.
Picovoice researchers published an open-source speaker recognition benchmark to give developers a head-start with evals. You can reproduce it or use your test data. If you're not familiar with the most used metrics in speaker recognition performance evaluations, check out the speaker recognition performance measurement and comparison guide.
Yes. Eagle Speaker Recognition is GDPR & HIPAA compliant. All processing is on-device—no voice data leaves the user's device, making it intrinsically compliant with any existing or future data privacy regulations.
Picovoice docs, blog, Medium posts, and GitHub are great resources to learn about voice AI, Picovoice technology, and how to detect who is speaking. Enterprise customers get dedicated support specific to their applications from Picovoice Product & Engineering teams. Reach out to your Picovoice contact or contact sales to discuss support options.