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.
1# Speaker Enrollment2o = pveagle.create_profiler(access_key)3while percentage < 100:4 percentage, feedback = o.enroll(5 get_next_enroll_audio_data())6speaker_profile = o.export()7
8# Speaker Recognition9eagle = pveagle.create_recognizer(10 access_key,11 speaker_profile)12while True:13 scores = eagle.process(14 get_next_audio_frame())1// Speaker Enrollment2const p = new EagleProfiler(3 accessKey);4while (percentage < 100) {5 const result: EnrollProgress =6 p.enroll(enrollAudioData);7 percentage = result.percentage;8}9const speakerProfile =10 p.export();11
12// Speaker Recognition13const e = new Eagle(14 accessKey,15 speakerProfile);16
17while (true) {18 const scores: number[] =19 e.process(audioData);20}1// Speaker Enrollment2EagleProfiler o = new EagleProfiler.Builder()3 .setAccessKey(accessKey)4 .build();5
6EagleProfilerEnrollResult result = null;7while (result != null && result.getPercentage() < 100.0) {8 result = o.enroll(getNextEnrollAudioData());9}10EagleProfile speakerProfile = o.export();11
12// Speaker Recognition13Eagle eagle = new Eagle.Builder()14 .setAccessKey(accessKey)15 .setSpeakerProfile(speakerProfile)16 .build();17
18while true {19 float[] scores = eagle.process(getNextAudioFrame());20}1// Speaker Enrollment2let o = try EagleProfiler(accessKey: accessKey)3while (percentage < 100.0) {4 (percentage, feedback) = o.enroll(5 pcm: get_next_enroll_audio_data())6}7let speakerProfile = try o.export()8
9// Speaker Recognition10let eagle = Eagle(11 accessKey: accessKey,12 speakerProfile: speakerProfile)13
14while true {15 let profileScores = try eagle.process(16 pcm: get_next_audio_frame())17}1// Speaker Enrollment2pv_eagle_profiler_t *eagle_profiler = NULL;3pv_eagle_profiler_init(access_key, model_path, &eagle_profiler);4 5float enroll_percentage = 0.0f;6pv_eagle_profiler_enroll(7 eagle_profiler,8 get_next_enroll_audio_frame(),9 get_next_enroll_audio_num_samples(),10 &feedback,11 &enroll_percentage);12int32_t profile_size_bytes = 0;13void *speaker_profile = malloc(profile_size_bytes);14pv_eagle_profiler_export(eagle_profiler, speaker_profile);15
16// Speaker Recognition17pv_eagle_t *eagle = NULL;18pv_eagle_init(19 access_key,20 model_path,21 1,22 &speaker_profile,23 &eagle);24 25float score = 0.f;26pv_eagle_process(eagle, pcm, &score);1// Speaker Enrollment2const eagleProfiler = 3 await EagleProfilerWorker.create(4 accessKey,5 eagleModel);6while (percentage < 100) {7 const result: EagleProfilerEnrollResult =8 await eagleProfiler.enroll(9 enrollAudioData);10 percentage = result.percentage;11}12const speakerProfile: EagleProfile =13 eagleProfiler.export();14
15// Speaker Recognition16const eagle = await EagleWorker.create(17 accessKey,18 eagleModel,19 speakerProfile);20
21while (true) {22 const scores: number[] =23 await eagle.process(audioData);24}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.
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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:
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, SpeechBrain, and WeSpeaker. 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. While Picovoice customers reach out to their contacts, prospects can also purchase Enterprise Support before committing to any paid plan.