Discriminating semi-continuous HMM for speaker verification

This paper describes the use of a multiple codebook SCHMM speaker verification system, which uses a novel technique for discriminative hidden Markov modelling known as discriminative observation probabilities (DOP). DOP can easily be added to a multiple codebook HMM system and require minimal additional computation and no additional training. The DOP technique can be applied to both speech and speaker recognition. Results are presented for text-dependent experiments on isolated digits from 27 true speakers and 84 casual imposters, recorded over the public telephone network in the United Kingdom. DOP are shown to significantly improve speaker verification performance for several commonly used parameter sets

Robust speaker identification using artificial neural networks

This research mainly focuses on recognizing the speakers through their speech samples. Numerous Text-Dependent or Text-Independent algorithms have been developed by people so far, to recognize the speaker from his/her speech. In this thesis, we concentrate on the recognition of the speaker from the fixed text i.e. Text-Dependent . Possibility of extending this method to variable text i.e. Text-Independent is also analyzed. Different feature extraction algorithms are employed and their performance with Artificial Neural Networks as a Data Classifier on a fixed training set is analyzed. We find a way to combine all these individual feature extraction algorithms by incorporating their interdependence. The efficiency of these algorithms is determined after the input speech is classified using Back Propagation Algorithm of Artificial Neural Networks. A special case of Back Propagation Algorithm which improves the efficiency of the classification is also discussed

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Phonetic Error Analysis Beyond Phone Error Rate

In this article, we analyse the performance of the TIMIT-based phone recognition systems beyond the overall phone error rate (PER) metric. We consider three broad phonetic classes (BPCs): {affricate, diphthong, fricative, nasal, plosive, semi-vowel, vowel, silence}, {consonant, vowel, silence} and {voiced, unvoiced, silence} and, calculate the contribution of each phonetic class in terms of the substitution, deletion, insertion and PER. Furthermore, for each BPC we investigate the following: evolution of PER during training, effect of noise (NTIMIT), importance of different spectral subbands (1, 2, 4, and 8 kHz), usefulness of bidirectional vs unidirectional sequential modelling, transfer learning from WSJ and regularisation via monophones. In addition, we construct a confusion matrix for each BPC and analyse the confusions via dimensionality reduction to 2D at the input (acoustic features) and output (logits) levels of the acoustic model. We also compare the performance and confusion matrices of the BLSTM-based hybrid baseline system with those of the GMM-HMM based hybrid, Conformer and wav2vec 2.0 based end-to-end phone recognisers. Finally, the relationship of the unweighted and weighted PERs with the broad phonetic class priors is studied for both the hybrid and end-to-end systems