Intermediate features are not useful for tone perception

Many theories assume that speech perception is done by first extracting features like the distinctive features, tonal features or articulatory gestures before recognizing phonetic units such as segments and tones. But it is unclear how exactly extracted features can lead to effective phonetic recognition. In this study we explore this issue by using support vector machine (SVM), a supervised machine learning model, to simulate the recognition of Mandarin tones from F0 in continuous speech. We tested how well a five-level system or a binary distinctive features system can identify Mandarin tones by training the SVM model with F0 trajectories with reduced temporal and frequency resolutions. At full resolution, the recognition rates were 97% and 86% based on the semitone and Hertz scales, respectively. At reduced temporal resolution, there was no clear decline in recognition rate until two points per syllable. At reduced frequency resolution, the recognition rate dropped rapidly: by the level with 5 bands, the accuracy was around 40% based on both Hertz and semitone scales. These results suggest that intermediate featural representations provide no benefit for tone recognition, and are unlikely to be critical for tone perception

Mandarin Tones Recognition by Segments of Fundamental Frequency Contours

ABSTRACT Mandarin is one of the tonal languages. In Mandarin tones, there are four lexical tones (tone 1 to tone 4) with four different fundamental frequency (f0), such as flat and high, rising, falling and then rising, and falling, respectively. In order to process signal on lexical tone, at first we have to identify which tone is. We would like to find out an efficient approach to identify Mandarin tones by the segments of fundamental frequency contours. In this study, 3 male and 3 female participants engaged in recording speech materials. All participants are native Mandarin speakers, no history of any speech or hearing disorder, and all passed articulation and voice assessment. There are two target syllables (/ti/ and /tu/) of four lexical tones used for the materials. In our experiment, we analysed the signal features and the acoustics characteristics that included the range of f0, the average of f0 and so on. Then we could predict which tone is from what the segments told. The result of this study revealed that the segments of the contours could not target the corresponding tone correctly. The approach of this study may not provide a way for hearing-devices to predict Mandarin tone before signal processing. The further study for prediction by segments of f0 contours is required

An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition

In the past, the kernel of automatic speech recognition (ASR) is dynamic time warping (DTW), which is feature-based template matching and belongs to the category technique of dynamic programming (DP). Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM-) like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation). A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW

Tone classification of syllable -segmented Thai speech based on multilayer perceptron

Thai is a monosyllabic and tonal language. Thai makes use of tone to convey lexical information about the meaning of a syllable. Thai has five distinctive tones and each tone is well represented by a single F0 contour pattern. In general, a Thai syllable with a different tone has a different lexical meaning. Thus, to completely recognize a spoken Thai syllable, a speech recognition system has not only to recognize a base syllable but also to correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system.;In this study, a tone classification of syllable-segmented Thai speech which incorporates the effects of tonal coarticulation, stress and intonation was developed. Automatic syllable segmentation, which performs the segmentation on the training and test utterances into syllable units, was also developed. The acoustical features including fundamental frequency (F0), duration, and energy extracted from the processing syllable and neighboring syllables were used as the main discriminating features. A multilayer perceptron (MLP) trained by backpropagation method was employed to classify these features. The proposed system was evaluated on 920 test utterances spoken by five male and three female Thai speakers who also uttered the training speech. The proposed system achieved an average accuracy rate of 91.36%

Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition

This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network

A comparison of features for large population speaker identification

Bibliography: leaves 95-104.Speech recognition systems all have one criterion in common; they perform better in a controlled environment using clean speech. Though performance can be excellent, even exceeding human capabilities for clean speech, systems fail when presented with speech data from more realistic environments such as telephone channels. The differences using a recognizer in clean and noisy environments are extreme, and this causes one of the major obstacles in producing commercial recognition systems to be used in normal environments. It is the lack of performance of speaker recognition systems with telephone channels that this work addresses. The human auditory system is a speech recognizer with excellent performance, especially in noisy environments. Since humans perform well at ignoring noise more than any machine, auditory-based methods are the promising approaches since they attempt to model the working of the human auditory system. These methods have been shown to outperform more conventional signal processing schemes for speech recognition, speech coding, word-recognition and phone classification tasks. Since speaker identification has received lot of attention in speech processing because of its waiting real-world applications, it is attractive to evaluate the performance using auditory models as features. Firstly, this study rums at improving the results for speaker identification. The improvements were made through the use of parameterized feature-sets together with the application of cepstral mean removal for channel equalization. The study is further extended to compare an auditory-based model, the Ensemble Interval Histogram, with mel-scale features, which was shown to perform almost error-free in clean speech. The previous studies of Elli to be more robust to noise were conducted on speaker dependent, small population, isolated words and now are extended to speaker independent, larger population, continuous speech. This study investigates whether the Elli representation is more resistant to telephone noise than mel-cepstrum as was shown in the previous studies, when now for the first time, it is applied for speaker identification task using the state-of-the-art Gaussian mixture model system