In this paper, we describe a novel acoustic model adaptation technique which generates “speaker-independent” HMM for the target environment. Recently, personal digital assistants like cellular phones are shifting to IP terminals. The encoding-decoding process utilized for transmitting over IP networks deteriorates the quality of speech data. This deterioration causes degradation in speech recog...

Automatic detection of phoneme boundaries is an important sub-task in building speech processing applications, especially text-to-speech synthesis (TTS) systems. The main drawback of the Gaussian mixture model hidden Markov model (GMMHMM) based forced-alignment is that the phoneme boundaries are not explicitly modeled. In an earlier work, we had proposed the use of signal processing cues in tan...

Recent years have been higher demands for automatic speech recognition (ASR) systems that are able to operate robustly in an acoustically noisy environment. This paper proposes an improved product hidden markov model (HMM) used for bimodal speech recognition. A two-dimensional training model is built based on dependently trained audio-HMM and visual-HMM, reflecting the asynchronous characterist...

Statistical parametric, especially Hidden Markov Model-based, text-tospeech (TTS) synthesis has received much attention recently. The quality of HMM-based speech synthesis approaches that of the state-of-the-art unit selection systems and possesses numerous favorable features, e.g. small runtime footprint, speaker interpolation, speaker adaptation. This paper presents the improvements of a Hung...

Statistical parametric speech synthesis has recently shown its ability to produce natural sounding speech while keeping a certain flexibility for voice transformation without requiring a huge amount of data. This abstract presents how machine learning techniques such as Hidden Markov Models in generation mode or context oriented clustering with decision trees are applied in speech synthesis. Fi...

In distant-talking speech recognition, the recognition accuracy is seriously degraded by reverberation and environmental noise. A robust speech recognition technique in such environments, HMM separation and composition, has been described in [1]. HMM separation estimates the model parameters of the acoustic transfer function using adaptation data uttered from an unknown position in noisy and re...

This paper proposes a new kind of hidden Markov model (HMM) based on multi-space probability distribution, and derives a parameter estimation algorithm for the extended HMM. HMMs are widely used statistical models for characterizing sequences of speech spectra, and have been successfully applied to speech recognition systems. HMMs are categorized into discrete HMMs and continuous HMMs, which ca...

This research reports the development of an HMM-based speech synthesis system for Malay, which is an underresourced language with few resources including recorded speech and segmental labels. We propose the cross-lingual use of resources for developing a Malay HMM-based speech synthesis system. We used the Festival English speech synthesis system to generate time-aligned phone transcriptions fo...

This paper presents an approach to the recognition of speech signal using frequency spectral information with Mel frequency for the improvement of speech feature representation in a HMM based recognition approach. A frequency spectral information is incorporated to the conventional Mel spectrum base speech recognition approach. The Mel frequency approach exploits the frequency observation for s...

We propose an information theoretic framework for quantitative assessment of acoustic modeling for hidden Markov model (HMM) based automatic speech recognition (ASR). Acoustic modeling yields the probabilities of HMM sub-word states for a short temporal window of speech acoustic features. We cast ASR as a communication channel where the input sub-word probabilities convey the information about ...