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In this paper, we propose a two-stage phone recognition system using articulatory and spectral features. In the first stage, articulatory features are predicted from spectral features using FeedForward Neural Networks (FFNNs). In the second stage, phone recognition is carried out using the predicted articulatory features and spectral features together. FFNNs and Hidden Markov Models are explored for...
The performance of Automatic Speech recognition system (ASR) built using close talk microphones degrades in noisy environments. AS R built using Throat Microphone (TM) speech shows relatively better performance under such adverse situations. However, some of the sounds are not well captured in TM. In this work we explore the combined use of Normal Microphone (NM) and TM features to improve the recognition...
Hidden factor such as gender characteristic plays an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). If there is a suppression process that represses the decrease of differences in acoustic-likelihood among categories resulted from gender factors, a robust ASR system can be realized. In our previous paper, we proposed a technique of gender effects...
Speaker-specific characteristics play an important role on the performance of Bangla (widely used as Bengali) automatic speech recognition (ASR). It is difficult to recognize speech affected by gender factors, especially when an ASR system contains only a single acoustic model. If there exists any suppression process that represses the decrease of differences in acoustic-likelihood among categories...
This paper presents a Neural Network-based Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: at first stage, a multilayer neural network (MLN) converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, where the second stage computes velocity (?) coefficients from the phoneme probabilities by using...
In this paper auditory like features MLPC and MFCC have been used as front-end and their performance has been evaluated on Aurora-2 database for Hidden Markov Model (HMM) based noisy speech recognition. The clean data set is used for training and test set A is used to examine the performance. It has been found that almost the same recognition performance has been obtained both for MLPC and MFCC and...
This paper proposes a new robust speech recognition method. Since the hidden Markov model (HMM) algorithm need a lot of training calculation, The dynamic time warping (DTW) algorithm based on median filter is used instead in our system. According to the short-term energy method, the non-speech segment can be removed. Recognition accuracy is thus improved. The cepstral mean subtraction (CMS), running...
In Korean language, a large proportion of word units are pronounced differently from their written forms due to an agglutinative and highly inflective nature having severe phonological phenomena and coarticulation effects. This paper reports on an ongoing study of Korean pronunciation modeling, in which the mapping between phonemic and orthographic units is modeled by a Bayesian network (BN). The...
This paper describes an isolated word recognition method based on distinctive phonetic features (DPFs). The method comprises two multilayer neural networks (MLNs). The first MLN, MLNLF-DPF, maps local features (LFs) of an input speech signal into discrete DPFs and the second MLN, MLNDyn, restricts dynamics of outputted DPFs by the MLNLF-DPF. In the experiments on Tohokudai Isolated Spoken-Word Database...
This article introduces automatic speech recognition based on Electro-Magnetic Articulography (EMA). Movements of the tongue, lips, and jaw are tracked by an EMA device, which are used as features to create Hidden Markov Models (HMM) and recognize speech only from articulation, that is, without any audio information. Also, automatic phoneme recognition experiments are conducted to examine the contribution...
A codebook design method for Hidden Markov Model (HMM) by using a Centroid Neural Network (CNN) is applied to a Korean monophone recognition problem in this paper. In order to alleviate the accuracy degradation problem in tied mixture HMM (TMHMM), this paper utilizes a clustering algorithm, called Centroid Neural Network with State Dependence measure (CNN(SD)), for TMHMMs. The CNN(SD) uses a novel...
In this paper Arabic alphadigits were investigated from the speech recognition problem point of view. Limited vocabulary Arabic Automatic Speech Recognition Systems (ASRs) were designed, implemented, and tested by using isolated word utterances which consists of Arabic alphabets and/or digits. These systems were implemented separately by using phoneme level and word level based HMM models in distinct...
Pattern recognition has long been a topic of fundamental importance in a wide range of science and technology. Over the years there have been a range of several tasks developed for speech recognition. While in recent years speech recognizer evaluation has focused on LVCSR research, we believe that evaluating recognition at the phone level is important since the words are always represented by the...
Before the advent of Hidden Markov Models(HMM)-based speech recognition, many speech applications were built using pattern matching algorithms like the Dynamic Time Warping (DTW) algorithm, which are generally robust to noise and easy to implement. The standard DTW algorithm usually suffers from lack of flexibility on start-end matching points and has high computational costs. Although some DTW-based...
Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and hidden Markov model (HMM) for recognition. The system is trained...
Multi pattern Viterbi algorithm (MPVA) to jointly decode and recognize multiple speech patterns for automatic speech recognition (ASR) is proposed. The MPVA is a generalization of the Viterbi algorithm (VA) to jointly decode multiple patterns for a given standard hidden Markov model (HMM). Unlike our previously proposed constrained multi pattern Viterbi algorithm (CMPVA), the MPVA does not require...
The automatic phonetic time-alignment of speech databases is essential for the development cycle of a text-to-speech (TTS) system. Furthermore, the quality of the synthesized speech signals is strongly related to the precision of the produced alignment. In the present work we study the performance of a new HMM-based speech segmentation method. The method is based on hybrid embedded and isolated-unit...
Stream weight training is one of the key issues in the bimodal integration for the audio-visual speech recognition. In this paper, the audio- and video-only HMM classifiers are combined to recognize audio-visual speech recognition. More specifically, a discriminative training method is provided, in which the state-dependent stream weights are trained based on lattice rescoring by the minimum phone...
This paper addresses the problem of using unstructured queries to search a structured database in voice search applications. By incorporating structural information in music metadata, the end-to-end search error has been reduced by 15% on text queries and up to 11% on spoken queries. Based on that, an HMM sequential rescoring model has reduced the error rate by 28% on text queries and up to 23% on...
The authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (sEMG) sensors in fixed recording locations on the face and neck. The original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech production. In this paper we systematically analyzed...
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