Abstract

Formant frequencies represent resonances of vocal tract system during the production of speech signals. Bandwidths associated with the formant frequencies are important parameters in analysis and synthesis of speech signals. In this paper, a method is proposed to extract the bandwidths associated with formant frequencies, by analysing short segments (2–3 ms) of speech signal. The method is based on two important properties of group delay function (GDF): (a) The GDF exhibits prominent peaks at resonant frequencies and (b) the influence of one resonant frequency on other resonances is negligible in GDF. The accuracy of the method is demonstrated for synthetic signals generated using all-pole filters. The method is evaluated by extracting bandwidths of synthetic signals in closed phase and open phase regions within a pitch period. The accuracy of the proposed method is also compared with that of two other methods, one based on linear prediction analysis of speech signals, and another based on filterbank arrays for obtaining amplitude envelopes and instantaneous frequency signals. Results indicate that the method based on the properties of GDF is suitable for accurate extraction of formant bandwidths, even from short segments of speech signal within a pitch period.

Abstract

In this paper we describe the development of Mandi Information System, a Telugu spoken dialogue system for obtaining price information of agricultural commodities like vegetables, fruits, pulses, spices, etc.. The target users of MIS are primarily the farmers in rural and semiurban areas. Speech recognition is error prone and it is necessary for the dialogue system to make minimum number of errors while acquiring information from a user and also to detect errors (if not correctable) and adopt appropriate strategies. In this paper we suggest an approach to improve the performance and usability of the system by using multiple decoders and contextual information

Abstract

Speech analysis is traditionally performed using short-time analysis to extract features in time and frequency domains. The window size for the analysis is fixed somewhat arbitrarily, mainly to account for the time varying vocal tract system during production. However, speech in its primary mode of excitation is produced due to impulse-like excitation in each glottal cycle. Anchoring the speech analysis around the glottal closure instants (epochs) yields significant benefits for speech analysis.Epoch-based analysis of speech helps not only to segment the speech signals based on speech production characteristics, but also helps in accurate analysis of speech.It enables extraction of important acoustic-phonetic features such as glottal vibra-tions, formants, instantaneous fundamental frequency, etc. Epoch sequence is useful to manipulate prosody in speech synthesis applications. Accurate estimation of epochshelps in characterizing voice quality features. Epoch extraction also helps in speech enhancement and multi speaker separation. In this tutorial article, the importance of epochs for speech analysis is discussed, and methods to extract the epoch informa-tion are reviewed. Applications of epoch extraction for some speech applications are demonstrated.

Abstract

Human-computer interaction plays a significant role for literate/illiterate and visually challenged users to access information. The mode of human-computer interaction could be speech, text, gestures, facial expressions, symbols etc., or a combination of these. An interaction could be in the form of a conversation including statements, questions, answers and expressions. A (uni-modal or multi-modal) conversational type of human-computer interaction is often referred to as a conversational system. A conversational system with speech as an input mode assumes significance as speech is a natural means of communication for human beings. The goal of a speech-based conversation (SBC) system is to provide information by conversing with a human-being in a natural fashion. Our objective is to develop speech based conversational systems for in formation access in an Indian language.

Abstract

For speaker recognition studies, it is necessary to process the speech signal suitably to capture the speaker-specific infor-mation. There is complementary speaker-specific information in the excitation source and vocal tract system characteristics.Therefore it is necessary to separate these components, even approximately, from the speech signal. We propose linear pre-diction (LP) residual and LP coefficients to represent these two components. Analysis is performed in a pitch synchronous manner in order to focus on the significant portion of the speech signal in each glottal cycle, and also to reduce the artifacts of digital signal processing on the extracted features. Finally, the speaker-specific information is captured from the excitation and the vocal tract system components using auto associative neural networks (AANN) models. We show that the pitch synchronous extraction of information from the residual and vocal tract sys-tem bring out the speaker-specific information much better than using the pitch asynchronous analysis as in the traditional block processing using an analysis window of fixed size

Abstract

The objective of this work is to characterize certain important features of excitation of speech, namely, detecting the regions of glottal activity and estimating the strength of excita-tion in each glottal cycle. The proposed method is based on the assumption that the excitation to the vocal-tract system can be ap- proximated by a sequence of impulses of varying strengths. The effect due to an impulse in the time-domain is spread uniformly across the frequency-domain including at zero-frequency. We propose the use of a zero-frequency resonator to extract the characteristics of excitation source from speech signals by filtering out most of the time-varying vocal-tract information. The regions of glottal activity and the strengths of excitation estimated from the speech signal are in close agreement with those observed from the simultaneously recorded electro-glotto-graph signals. The performance of the proposed glottal activity detection is evaluated under different noisy environments at varying levels of degradation.

Abstract

This paper examines the Lombard effect on the excitation fea-tures in speech production. These features correspond mostly to the acoustic features at sub segmental (