AI for Audio and Speech - Revision history

Wordpad: BloomWiki: AI for Audio and Speech

2026-04-25T01:45:57Z

BloomWiki: AI for Audio and Speech

← Older revision		Revision as of 01:45, 25 April 2026
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			<div style="background-color: #4B0082; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
	{{BloomIntro}}		{{BloomIntro}}
	AI for audio and speech encompasses the use of machine learning to process, understand, generate, and transform audio signals — including spoken language, music, environmental sounds, and biological signals. Speech recognition converts spoken words to text; text-to-speech synthesis creates natural-sounding voices; speaker identification recognizes who is speaking; music generation creates novel musical compositions; and sound classification identifies events from environmental audio. These technologies are embedded in virtual assistants, accessibility tools, music platforms, hearing aids, and security systems.		AI for audio and speech encompasses the use of machine learning to process, understand, generate, and transform audio signals — including spoken language, music, environmental sounds, and biological signals. Speech recognition converts spoken words to text; text-to-speech synthesis creates natural-sounding voices; speaker identification recognizes who is speaking; music generation creates novel musical compositions; and sound classification identifies events from environmental audio. These technologies are embedded in virtual assistants, accessibility tools, music platforms, hearing aids, and security systems.
			</div>

	== Remembering ==		__TOC__

			<div style="background-color: #000080; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Remembering</span> ==
	* '''Automatic Speech Recognition (ASR)''' — The conversion of spoken audio to text; also called speech-to-text (STT).		* '''Automatic Speech Recognition (ASR)''' — The conversion of spoken audio to text; also called speech-to-text (STT).
	* '''Text-to-Speech (TTS)''' — Synthesizing natural-sounding speech from written text.		* '''Text-to-Speech (TTS)''' — Synthesizing natural-sounding speech from written text.
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	* '''Vocoder''' — A model that converts acoustic features (mel spectrograms) to raw audio waveforms (WaveNet, HiFi-GAN).		* '''Vocoder''' — A model that converts acoustic features (mel spectrograms) to raw audio waveforms (WaveNet, HiFi-GAN).
	* '''Voice cloning''' — Synthesizing a new speaker's voice from a short sample; enabling personalized TTS.		* '''Voice cloning''' — Synthesizing a new speaker's voice from a short sample; enabling personalized TTS.
			</div>

	== Understanding ==		<div style="background-color: #006400; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Understanding</span> ==
	Audio is a continuous 1D waveform, typically sampled at 16,000–44,100 Hz. A 10-second speech clip at 16kHz = 160,000 samples — a long sequence. Direct learning from raw waveforms is possible (WaveNet, SoundStream) but computationally expensive. The dominant approach: convert to 2D time-frequency representations (spectrograms), then apply computer vision or sequence models.		Audio is a continuous 1D waveform, typically sampled at 16,000–44,100 Hz. A 10-second speech clip at 16kHz = 160,000 samples — a long sequence. Direct learning from raw waveforms is possible (WaveNet, SoundStream) but computationally expensive. The dominant approach: convert to 2D time-frequency representations (spectrograms), then apply computer vision or sequence models.

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	The voice cloning challenge: Modern TTS systems like VALL-E can clone a voice from 3 seconds of audio, generating speech in that voice from any text. This enables transformative accessibility applications and severe deepfake risks simultaneously.		The voice cloning challenge: Modern TTS systems like VALL-E can clone a voice from 3 seconds of audio, generating speech in that voice from any text. This enables transformative accessibility applications and severe deepfake risks simultaneously.
			</div>

	== Applying ==		<div style="background-color: #8B0000; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Applying</span> ==
	'''Speech recognition with Whisper:'''		'''Speech recognition with Whisper:'''
	<syntaxhighlight lang="python">		<syntaxhighlight lang="python">
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	: '''Sound classification''' → YAMNet, PANNs, BirdNET (birds)		: '''Sound classification''' → YAMNet, PANNs, BirdNET (birds)
	: '''Speaker diarization''' → pyannote.audio, NVIDIA NeMo		: '''Speaker diarization''' → pyannote.audio, NVIDIA NeMo
			</div>

	== Analyzing ==		<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Analyzing</span> ==
	{\| class="wikitable"		{\| class="wikitable"
	\|+ ASR System Comparison		\|+ ASR System Comparison
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	'''Failure modes''': ASR performance degrades severely for accented speech, noisy environments, overlapping speakers, and domain-specific vocabulary (medical, legal, technical). WER on standard benchmarks (LibriSpeech) is much lower than real-world deployment WER. TTS systems can produce unnatural prosody for unusual names, numbers, and non-standard text. Voice cloning enables deepfake audio — voice authentication systems must be robust to synthetic voices.		'''Failure modes''': ASR performance degrades severely for accented speech, noisy environments, overlapping speakers, and domain-specific vocabulary (medical, legal, technical). WER on standard benchmarks (LibriSpeech) is much lower than real-world deployment WER. TTS systems can produce unnatural prosody for unusual names, numbers, and non-standard text. Voice cloning enables deepfake audio — voice authentication systems must be robust to synthetic voices.
			</div>

	== Evaluating ==		<div style="background-color: #483D8B; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Evaluating</span> ==
	ASR evaluation: (1) WER on diverse benchmarks: LibriSpeech (read speech), Switchboard (telephone conversational), CHiME-4 (noisy), AMI (meeting), and target domain. (2) Latency: Real-time factor (RTF): if RTF=0.1, model processes 10 seconds of audio per second of compute — production systems typically require RTF<1. (3) Fairness: WER disaggregated by accent, dialect, gender, and age — ASR systems frequently exhibit higher WER for underrepresented groups. TTS evaluation: MOS (Mean Opinion Score) from human listeners; naturalness and intelligibility scores separately.		ASR evaluation: (1) WER on diverse benchmarks: LibriSpeech (read speech), Switchboard (telephone conversational), CHiME-4 (noisy), AMI (meeting), and target domain. (2) Latency: Real-time factor (RTF): if RTF=0.1, model processes 10 seconds of audio per second of compute — production systems typically require RTF<1. (3) Fairness: WER disaggregated by accent, dialect, gender, and age — ASR systems frequently exhibit higher WER for underrepresented groups. TTS evaluation: MOS (Mean Opinion Score) from human listeners; naturalness and intelligibility scores separately.
			</div>

	== Creating ==		<div style="background-color: #2F4F4F; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">
			== <span style="color: #FFFFFF;">Creating</span> ==
	Designing a production speech AI pipeline: (1) ASR: deploy faster-whisper with CTranslate2 on GPU for batch; Vosk or Sherpa-ONNX for on-device/edge. (2) VAD (Voice Activity Detection): silero-VAD to filter silent segments before ASR — reduces cost and latency. (3) Diarization: pyannote.audio for speaker labeling. (4) Post-processing: domain-specific word correction (medical terminology, product names) using a custom language model or dictionary lookup. (5) TTS: ElevenLabs API or Coqui TTS for synthesis; cache common phrases. (6) Quality monitoring: compute WER on human-transcribed sample weekly; alert if WER increases >20% relative.		Designing a production speech AI pipeline: (1) ASR: deploy faster-whisper with CTranslate2 on GPU for batch; Vosk or Sherpa-ONNX for on-device/edge. (2) VAD (Voice Activity Detection): silero-VAD to filter silent segments before ASR — reduces cost and latency. (3) Diarization: pyannote.audio for speaker labeling. (4) Post-processing: domain-specific word correction (medical terminology, product names) using a custom language model or dictionary lookup. (5) TTS: ElevenLabs API or Coqui TTS for synthesis; cache common phrases. (6) Quality monitoring: compute WER on human-transcribed sample weekly; alert if WER increases >20% relative.

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	[[Category:Speech Processing]]		[[Category:Speech Processing]]
	[[Category:Deep Learning]]		[[Category:Deep Learning]]
			</div>

Wordpad: New BloomWiki article: AI for Audio and Speech

2026-04-23T06:46:22Z

New BloomWiki article: AI for Audio and Speech

New page

{{BloomIntro}}
AI for audio and speech encompasses the use of machine learning to process, understand, generate, and transform audio signals — including spoken language, music, environmental sounds, and biological signals. Speech recognition converts spoken words to text; text-to-speech synthesis creates natural-sounding voices; speaker identification recognizes who is speaking; music generation creates novel musical compositions; and sound classification identifies events from environmental audio. These technologies are embedded in virtual assistants, accessibility tools, music platforms, hearing aids, and security systems.

== Remembering ==
* '''Automatic Speech Recognition (ASR)''' — The conversion of spoken audio to text; also called speech-to-text (STT).
* '''Text-to-Speech (TTS)''' — Synthesizing natural-sounding speech from written text.
* '''Speaker diarization''' — Segmenting an audio recording by speaker identity: "who spoke when."
* '''Speaker verification''' — Determining whether a voice sample belongs to a claimed identity.
* '''Speaker identification''' — Identifying which person from a known set is speaking.
* '''Waveform''' — The raw audio signal as a 1D array of amplitude values over time.
* '''Spectrogram''' — A 2D time-frequency representation of audio; shows which frequencies are present at each moment.
* '''Mel spectrogram''' — A spectrogram with frequency axis mapped to the mel scale, which approximates human auditory perception.
* '''MFCC (Mel-Frequency Cepstral Coefficients)''' — Features computed from the mel spectrogram widely used in traditional speech processing.
* '''End-to-end ASR''' — Models that directly map audio to text without separate acoustic and language model stages (Whisper, Wav2Vec).
* '''Wav2Vec 2.0''' — A self-supervised pre-training approach for speech that learns speech representations from raw audio, enabling powerful ASR with limited labeled data.
* '''Whisper''' — OpenAI's multilingual, multitask speech recognition model trained on 680k hours of web audio; state-of-the-art open ASR.
* '''WER (Word Error Rate)''' — The primary metric for ASR: (Substitutions + Deletions + Insertions) / Total Reference Words.
* '''Vocoder''' — A model that converts acoustic features (mel spectrograms) to raw audio waveforms (WaveNet, HiFi-GAN).
* '''Voice cloning''' — Synthesizing a new speaker's voice from a short sample; enabling personalized TTS.

== Understanding ==
Audio is a continuous 1D waveform, typically sampled at 16,000–44,100 Hz. A 10-second speech clip at 16kHz = 160,000 samples — a long sequence. Direct learning from raw waveforms is possible (WaveNet, SoundStream) but computationally expensive. The dominant approach: convert to 2D time-frequency representations (spectrograms), then apply computer vision or sequence models.

**ASR evolution**: Early ASR combined separate acoustic models (HMMs + Gaussian mixtures) and language models. Deep learning replaced the acoustic model with neural networks but kept the pipeline. Modern end-to-end models (CTC-based, attention-based encoders) jointly learn acoustic-to-linguistic mapping. Whisper uses a transformer encoder-decoder, treating ASR as a sequence-to-sequence translation problem, and achieves remarkable robustness across accents, noise, and languages.

**Self-supervised audio**: Wav2Vec 2.0 pre-trains on unlabeled audio by learning to identify the correct quantized speech representation for a masked segment among distractors. This contrastive objective learns rich speech representations without transcriptions. Fine-tuning on even 10 minutes of labeled audio achieves competitive ASR — transformative for low-resource languages.

**TTS pipeline**: Text → phonemes → acoustic features (mel spectrogram) → waveform. Tacotron 2 generates mel spectrograms from phoneme sequences. HiFi-GAN (vocoder) converts mel spectrograms to high-fidelity audio. Modern neural TTS is nearly indistinguishable from human speech in quality evaluations.

**The voice cloning challenge**: Modern TTS systems like VALL-E can clone a voice from 3 seconds of audio, generating speech in that voice from any text. This enables transformative accessibility applications and severe deepfake risks simultaneously.

== Applying ==
'''Speech recognition with Whisper:'''
<syntaxhighlight lang="python">
import whisper
import numpy as np

# Load model (tiny/base/small/medium/large)
model = whisper.load_model("medium")

# Transcribe audio file (handles any language automatically)
result = model.transcribe(
"interview.mp3",
language="en", # Auto-detect if None
task="transcribe", # Or "translate" to English
word_timestamps=True, # Return per-word timing
fp16=True # Use FP16 for speed
)
print(result["text"])
print(result["segments"]) # [{start, end, text, words}]

# Streaming ASR with faster-whisper (CTranslate2 optimized)
from faster_whisper import WhisperModel
model = WhisperModel("large-v3", device="cuda", compute_type="int8")
segments, info = model.transcribe("audio.wav", beam_size=5)
for segment in segments:
print(f"[{segment.start:.2f}s → {segment.end:.2f}s] {segment.text}")
</syntaxhighlight>

'''Real-time speaker diarization with pyannote:'''
<syntaxhighlight lang="python">
from pyannote.audio import Pipeline

pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1",
use_auth_token="YOUR_TOKEN")
diarization = pipeline("meeting.wav", num_speakers=3)
for turn, _, speaker in diarization.itertracks(yield_label=True):
print(f"[{turn.start:.1f}s → {turn.end:.1f}s] {speaker}")
</syntaxhighlight>

; Audio AI application map
: '''Transcription''' → Whisper (offline), AssemblyAI, Rev.ai (API)
: '''Real-time ASR''' → faster-whisper, DeepSpeech, Vosk (edge)
: '''TTS''' → ElevenLabs (realistic), Coqui TTS (open), Kokoro (lightweight)
: '''Music generation''' → MusicGen (Meta), Suno, Udio
: '''Sound classification''' → YAMNet, PANNs, BirdNET (birds)
: '''Speaker diarization''' → pyannote.audio, NVIDIA NeMo

== Analyzing ==
{| class="wikitable"
|+ ASR System Comparison
! System !! WER (LibriSpeech clean) !! Languages !! Latency !! License
|-
| Whisper large-v3 || 2.7% || 99 || Batch || Open (MIT)
|-
| faster-whisper || Same (optimized) || 99 || Near-real-time || Open
|-
| Google Cloud STT || ~3% || 130+ || Real-time || Proprietary/paid
|-
| AssemblyAI || ~3% || 20 || Real-time API || Proprietary/paid
|-
| Wav2Vec 2.0 || ~2.2% (fine-tuned) || Multilingual | Batch || Open (Apache)
|}

'''Failure modes''': ASR performance degrades severely for accented speech, noisy environments, overlapping speakers, and domain-specific vocabulary (medical, legal, technical). WER on standard benchmarks (LibriSpeech) is much lower than real-world deployment WER. TTS systems can produce unnatural prosody for unusual names, numbers, and non-standard text. Voice cloning enables deepfake audio — voice authentication systems must be robust to synthetic voices.

== Evaluating ==
ASR evaluation: (1) **WER on diverse benchmarks**: LibriSpeech (read speech), Switchboard (telephone conversational), CHiME-4 (noisy), AMI (meeting), and target domain. (2) **Latency**: Real-time factor (RTF): if RTF=0.1, model processes 10 seconds of audio per second of compute — production systems typically require RTF<1. (3) **Fairness**: WER disaggregated by accent, dialect, gender, and age — ASR systems frequently exhibit higher WER for underrepresented groups. TTS evaluation: MOS (Mean Opinion Score) from human listeners; naturalness and intelligibility scores separately.

== Creating ==
Designing a production speech AI pipeline: (1) ASR: deploy faster-whisper with CTranslate2 on GPU for batch; Vosk or Sherpa-ONNX for on-device/edge. (2) VAD (Voice Activity Detection): silero-VAD to filter silent segments before ASR — reduces cost and latency. (3) Diarization: pyannote.audio for speaker labeling. (4) Post-processing: domain-specific word correction (medical terminology, product names) using a custom language model or dictionary lookup. (5) TTS: ElevenLabs API or Coqui TTS for synthesis; cache common phrases. (6) Quality monitoring: compute WER on human-transcribed sample weekly; alert if WER increases >20% relative.

[[Category:Artificial Intelligence]]
[[Category:Speech Processing]]
[[Category:Deep Learning]]