Author: Anna Sánchez Espunyes, Kenza Bouzid

Solutions for labs for SSR course @ KTH. Each lab contains implementation of speech recognition algorithms as well as notebooks with experiments.

The objective is to experiment with different features commonly used for speech analysis and recognition.

• compute Mel Filterbank and MFCC features step-by-step
• examine features
• evaluate correlation between feature
• compare utterances with Dynamic Time Warping
• illustrate the discriminative power of the features with respect to words
• perform hierarchical clustering of utterances
• train and analyze a Gaussian Mixture Model of the feature vectors.

• implement the algorithms for the evaluation and decoding of Hidden Markov Models (HMMs),
• use your implementation to perform isolated word recognition
• implement the algorithms for training Gaussian Hidden Markov Models (G-HMMs),
• explain the meaning of the forward, backward and state posterior probabilities evaluated on speech utterances,

The overall task is to implement and test methods for isolated word recognition:

• combine phonetic HMMs into word HMMs using a lexicon
• implement the forward-backward algorithm,
• use it compute the log likelihood of spoken utterances given a Gaussian HMM
• perform isolated word recognition
• implement the Viterbi algorithm, and use it to compute Viterbi path and likelihood
• compare and comment Viterbi and Forward likelihoods
• implement the Baum-Welch algorithm to update the parameters of the emission probability distributions

• create phonetic annotations of speech recordings using predefined phonetic models
• use software libraries1 to define and train Deep Neural Networks (DNNs) for phoneme recognition
• explain the difference between HMM and DNN training
• compare which speech features are more suitable for each model and explain why

• using predefined Gaussian-emission HMM phonetic models, create time aligned phonetic transcriptions of the TIDIGITS database,
• define appropriate DNN models for phoneme recognition using Keras,
• train and evaluate the DNN models on a frame-by-frame recognition score,
• repeat the training by varying model parameters and input features