In this paper, two sets of phonetically balanced words (PBW) in Filipino were developed; namely the 2syllable, and 3-syllable PBW list. These are tested as a speech corpus in a word-level recognizer using the Hidden Markov Model (HMM) as a framework and Mel-Frequency Cepstral Coefficient (MFCC) as a feature extraction technique. Thus, this study is a preparation for a Largecorpus Filipino Langu...

This paper investigates the adaptation of automatic speech recognition to disease detection by analyzing the voice parameters. The analysis of the voice allows the identification of the diseases which affect the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. This paper presents a novel method to keep track of patient’s patholog...

proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral-based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to ...

A modeling approach to auditory speech analysis and recognition is proposed and evaluated, where a composite auditory model is used to generate parallel sets of auditory-nerve instantaneous firing rates (IFR’s) along the spatial dimension, followed by a processing stage that constructs from the IFR’s an interval statistics in a form called the interpeak interval histogram (IPIH). A speech prepr...

This work proposes a method of predicting pitch and voicing from mel-frequency cepstral coefficient (MFCC) vectors. Two maximum a posteriori (MAP) methods are considered. The first models the joint distribution of the MFCC vector and pitch using a Gaussian mixture model (GMM) while the second method also models the temporal correlation of the pitch contour using a combined hidden Markov model (...

The mel frequency cepstral coefficient (MFCC) is one of the most important features required among various kinds of speech applications. In this paper, the first chip for speech features extraction based on MFCC algorithm is proposed. The chip is implemented as an intellectual property, which is suitable to be adopted in a speech recognition system on a chip. The computational complexity and me...

The problem of context recognition from mobile audio data is considered. We consider ten different audio contexts (such as car, bus, office and outdoors) prevalent in daily life situations. We choose mel-frequency cepstral coefficient (MFCC) parametrization and present an extensive comparison of six different classifiers: knearest neighbor (kNN), vector quantization (VQ), Gaussian mixture model...

This paper presents a new feature vector set for noisy speech recognition in autocorrelation domain. The autocorrelation domain is well known for its pole preserving and noise separation properties. In this paper we will use the autocorrelation domain as an appropriate candidate for robust feature extraction. In our approach, extraction of mel frequency cepstral coefficients (MFCC) of the speec...

In this paper we present a fast and efficient match algorithm, which consists of two key techniques: Spectral Correlation Based Feature Merge(SCBFM) and Two-Step Retrieval(TSR). SCBFM can remove the redundant information. In consequence, the resulting feature sequence has a smaller size, requiring less storage and computation. In addition, most of the tempo variation is removed; thus a much sim...

This paper investigates the adaptation of automatic speech recognition to disease detection by analyzing the voice parameters. The analysis of the voice allows the identification of the diseases which affect the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. This paper presents a novel method to keep track of patient’s patholog...