Speech & Language Blog

Introduction This post summarises acoustic features for various tasks of speech processing. Automatic speech recognition (ASR) is one of the most studied speech processing tasks. Acoustic features for ASR include Mel-frequency cepstrum coefficients (MFCCs) and spectogram-based features including Mel-spectrograms and Mel-filter banks. The choice of acoustic features depends on a choice of ASR model: Traditional machine learning (ML) models such as Gaussian mixture models (GMMs) have difficulties of handling correlated features and MFCCs are favourite for de-correlated coeffcients. More recent deep learning based models e.g., Conformer use acoustic feature vectors with correlation between neighbour dimensions: filterbanks. A popular model from OpenAI, Whisper , directly takes as input a log-Mel spectrogram which is technically the same representation as filterbanks (will be explained later). ...

Speech Visualisation This post covers visualisation of a speech signal: plotting a waveform, annotating a waveform and showing speech spectrums. I am using the first speech file (BASIC5000_0001) of the JSUT corpus  that consists of 10 hour recordings of a Japanese female speaker. JSUT ver 1.1 BASIC5000_0001 My code is all written in this Python notebook: https://github.com/yasumori/blog/blob/main/2026/2026_01_visualisation.ipynb . You should be able to run it after installing required libraries: librosa, matplotlib, and numpy. The first speech file is also uploaded to my GitHub, following the terms of use "Re-distribution is not permitted, but you can upload a part of this corpus (e.g., ~100 audio files) in your website or blog". import librosa import subprocess # load audio in 16kHz signal, sr = librosa.load("./data/BASIC5000_0001.wav", sr=16000) print(f"number of samples: {len(signal)}") print(f"duration {len(signal)/sr...