SpeechAlgorithms: a catalogue of DSP and speech code to read and reuse

A table of algorithms, not a library

The repository is presented as a content list rather than as a project with an API. The README opens by saying that the implementation of various speech algorithms can be downloaded individually through DownGit, which is a service that lets you download a subdirectory of a GitHub repository as a zip. That single sentence defines the whole design. There is no build system, no package manager manifest, no shared library and no version to depend on. What there is, is a table per domain with a title and a link into a subdirectory, and the top-level listing confirms it: AcousticEchoCancellation, AudioGainControl, AudioNoiseReduction, AudioPatternReognition, AudioProcess, DesignSound, MicrophoneArray, Resample, SpeechCodec, SpeechEnhancement, SpeechMetrics and VoiceActivityDetection, plus a second README in Chinese. The unit of reuse is a folder.

Signal processing basics sit in AudioProcess

The AudioProcess directory is where the fundamentals live, and the list is a good syllabus for speech signal processing. There is resampling of a speech signal at an arbitrary sample rate in its own top-level Resample directory, separate from the others. Inside AudioProcess there is audio alignment with cross-correlation, which is how you line up two recordings of the same speech, and a music recognition system based on audio fingerprinting. The Goertzel algorithm is there, which is the efficient way to detect a single frequency rather than a whole spectrum, and it is the classic building block for a DTMF detector. There is voice speed and pitch change, embedding and extracting audio digital watermarking, and the trio of enframe, windowing and DFT, which is the analysis front end that everything else in the field assumes. Then two that are less common in textbooks: a CTC prefix beam search, which is the decoding step in a speech recognition pipeline, and speech similarity evaluation with dynamic time warping, which is how you compare two utterances of different lengths.

Three WebRTC directories that show the same problems solved three ways

One of the more instructive features of the catalogue is that three problems appear twice, once as a WebRTC implementation and once as something else, and the pairing makes the subject teachable. Acoustic echo cancellation appears as an introduction to adaptive filter LMS cancellation, as a Kalman filter based echo cancellation algorithm, and as an introduction to WebRTC AEC, in three separate directories. Automatic gain control appears only as the WebRTC AGC introduction. Noise reduction appears as speech enhancement using spectral subtraction, as transient noise suppression, and as the WebRTC ANR introduction. WebRTC AEC is what every browser and every conferencing stack uses, and the LMS version is the textbook adaptive filter formulation, so reading the pair tells you what the shipped implementation actually had to add. Spectral subtraction is the classical single-channel noise removal method and the WebRTC ANR is what runs in production.

Voice activity detection, two ways, plus a codec for labels

The VoiceActivityDetection directory contains an introduction to WebRTC VAD and endpoint detection using an LSTM, and the LSTM entry has a subdirectory of its own, VADCoder, for generating VAD labels using the AMR codec. The structure is instructive on its own. Endpointing, meaning deciding that the speaker has finished, is the problem WebRTC VAD solves with a statistical model over the signal, and the LSTM version solves it with a learned one. But training a supervised model needs labels, and labels need a reference, so the VADCoder generates them by running the AMR codec over the audio and using its own silence and speech decisions as the annotation. The trick of using the codec to generate labels for the model that replaces the codec is the kind of practical detail that a book chapter tends to skip.

Beamforming and localisation for microphone arrays

The MicrophoneArray directory covers the multi-microphone half of the field, and the entry names are specific enough to be useful. There is CGMM-MVDR under a Beamforming subdirectory, which is a blind beamforming approach that estimates the acoustic environment as a complex Gaussian mixture and then derives minimum-variance distortionless response filters from it. Sound source localisation appears with two different principles: based on time difference of arrival, which compares when a signal reaches each microphone, and based on the steered response power with phase transform, which is the phase-compensated way to compare microphone signals for direction. The SRP-PHAT pairing is the more robust of the two in noise, which is exactly why the two are listed side by side rather than as alternatives.

Enhancement, codecs, metrics and the PESQ binary

The remaining directories cover the rest of the chain. SpeechEnhancement holds generating speech samples with noise, echo, reverberation and howling, single channel speech enhancement using a DNN, speech separation based on a time-frequency mask, and data augmentations for speech enhancement, with the last two pointing out to a separate repository, noise-xorcist, for the datasets and for how to generate howling samples. SpeechCodec contains the AI speech codec Lyra and the G.711 codec. SpeechMetrics contains speech quality metrics and speech intelligibility metrics, which are the objective numbers you report when you change a codec or an enhancer. Two items outside the directory list are worth noting. One is a second README, README_CN.md, so the project is documented in Chinese and English. The other is pesq.exe at the repository root, a Windows executable for perceptual speech quality evaluation, checked in rather than built, which tells you something about the audience: a Windows user running MATLAB or Python on the same machine.

Where SpeechAlgorithms is the wrong choice

Four limits apply. There is no package, no build system and no test suite, so you are copying code into your own tree and maintaining it from there, and the README gives no build instructions for the individual directories. There is no versioning either, since the repository has no GitHub releases, so a directory can change under you when you pull, and the only way to pin a revision is to pin a commit. The code is C, which means it is not callable from Python, JavaScript or Go without a binding you write, and several of the entries are research implementations whose numerical behaviour on real recordings is unverified here. And the licences are mixed in a way the top-level file does not fully resolve: the repository says Apache-2.0, while the WebRTC-derived directories descend from code with its own licensing, and the embedded third-party binaries such as the PESQ executable have separate terms again. For anything that ships, treat each directory as a separate licensing question.

Editorial conclusion

Use SpeechAlgorithms when you need to understand how a specific speech technique works before you reach for a library, since each entry is a self-contained implementation you can read rather than a wrapper you have to trust. Do not adopt it as a production dependency, because there is no package, no build system, no test suite and no versioned release, so you are copying code rather than linking it. Verify first which algorithm you actually need against the directory names, whether the directory you want includes its own sample data and build instructions, since the README does not describe them, and that the licence covers your use, since the licence field on the repository says Apache-2.0 while the WebRTC-derived parts may carry their own upstream terms.

Frequently asked questions

Official sources

1. Issues
2. License: Apache-2.0
3. README
4. Ryuk17/SpeechAlgorithms on GitHub