Speech is at the core of human communication. Speaking and listing comes so natural to us that we do not have to think about it at all. The underlying cognitive processes are very rapid and almost completely subconscious. It is hard, if not impossible not to understand speech. For computers on the other hand, recognising speech is a daunting task. It has to deal with a large number of different voices "influenced, among other things, by emotion, moods and fatigue" the acoustic properties of different environments, dialects, a huge vocabulary and an unlimited creativity of speakers to combine words and to break the rules of grammar. Almost all existing automatic speech recognisers use statistics over speech sounds "what is the probability that a piece of audio is an a-sound" and statistics over word combinations to deal with this complexity. The results of those systems are impressive but unfortunately not good enough for most applications of speech recognition. This thesis proposes to put context information in the models of speech recognition to achieve better recognition results. Context is defined as knowledge of the speaker, such as gender and dialect, knowledge of the conversation and knowledge of the world. The influence of each of those categories is investigated using data analysis and case studies and new models for speech recognition are defined. In particular, a model that dynamically adapts the vocabulary of the recogniser to the topic of a conversation, which it can automatically determine, is presented.

/pdf/modelling-context-in-automatic-speech-recognition-3g9peiqngr.pdf

A new state-dependent phonetic tied-mixture model with head-body-tail structured HMM for real-time continuous phoneme recognition system

Modelling context in automatic speech recognition

This work describes a real-time lip-sync method using which an avatar's lip shape is synchronized with the corresponding speech signal. Phoneme recognition is generally regarded as an important task in the operation of a real-time lip-sync system. In this work, the use of the Head-Body-Tail (HBT) model is proposed for the purpose of more efficiently recognizing phonemes which are variously uttered due to co-articulation effects. The HBT model effectively deals with the transition parts of context-dependent models for small-sized vocabulary tasks. These models provide better recognition performance than general context-dependent or context-independent models for the task of digit or vowel recognition. Moreover, each phoneme is categorized into one among four classes and the class-dependent codebook is generated to further improve the performance. Additionally, for the clear representation of the context dependency information in the transient parts, some Gaussians are excluded from class-dependent codebook. The proposed method leads to a lip-sync system that performs at a level that is similar to previous designs based on HBT and continuous hidden Markov models (CHMMs). However, our method reduces the number of model parameters by one-third and enables real-time operation.

Real-Time Continuous Phoneme Recognition System Using Class-Dependent Tied-Mixture HMM With HBT Structure for Speech-Driven Lip-Sync

Automatyczna identyfikacja cyfr dla mówców polskojęzycznych

The problem of communication between a man and a machine is very old. First constructors had to decide how to transmit information from a man to machine and vice versa. This problem still exists and each engineer who designs a new device must decide how the communication between operator and a machine will be done. Simple devices use buttons and Light Emit Diodes [LED], more complicated – keyboards and screens. Fast technical development and numerous scientific research allowed to use also voice for this purpose. Here there are two different problems: voice producing and voice recognition. The first one is not very difficult, in the simplest case the machine could record a set of words which would be used for communication. Nowadays there are specialised integrated circuits (speech processors) which enable recording and reproducing whole words and sentences. The second problem – voice recognition is more complicated. First of all people are different and say the same words in a different way. Secondly, the way of speaking depends on many aspects like health of the speaker, his mood or emotion. Thirdly, we are living in noisy environment so usually together with speech signal we also obtain different noises. There are a lot of different methods used for automatic speech recognition. The most popular is HMM – Hidden Markov Model (Junho & Hanseok, 2006; Wydra, 2007; Kumar & Sreenivas 2005; Ketabdar at al., 2005) , which uses sequences of events (states), where the probability of being in each state and the probability of transition to the other states are counted. Each state is described by many, mostly spectral parameters. There are also other, less popular methods used for automatic speech recognition like Neural Network Method (Vali at al., 2006; Holmberg at al.,2005; Togneri & Deng, 2007), Audio-visual Method (Seymour at al., 2007; Hueber at al., 2007) and many others (Nishida et al., 2005). Nowadays there is a possibility to achieve more than 90% accuracy in automatic speech recognition. HMM method, although the most popular, is very complicated. Each state is described by the matrix with many spectral, cepstral and linear prediction parameters. It causes the need for many analyses and calculations during the automatic recognition process. In this chapter the author shows a new approach to this problem. The new method described here is simpler and faster than HMM method and gives similar or better results in speech recognition accuracy. Although it was tested in Polish, the rules can be adopted to other languages.

methods

/pdf/speech-recognition-based-on-the-grid-method-and-image-2ben8xa8fx.pdf

A new state-dependent phonetic tied-mixture model with head-body-tail structured HMM for real-time continuous phoneme recognition system

Citations

Modelling context in automatic speech recognition

Real-Time Continuous Phoneme Recognition System Using Class-Dependent Tied-Mixture HMM With HBT Structure for Speech-Driven Lip-Sync

Automatyczna identyfikacja cyfr dla mówców polskojęzycznych

Speech Recognition Based on the Grid Method and Image Similarity

Automatyczne rozpoznawanie cyfr w języku polskim - identyfikacja fonemów szumowych

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