Abstract

Most of the existing vowel onset point (VOP) detection methods are developed for clean speech. In this chapter, we propose methods for detection of VOPs in the presence of speech coding and background noise conditions. VOP detection method for coded speech is based on the spectral energy between 500 and 2,500 Hz frequency band of the speech segments present in glottal closure region. In case of noisy speech, the proposed VOP detection method exploits the spectral energy at the formant locations of the speech segments present in glottal closure region. The proposed VOP detection methods are evaluated using objective measures and consonant vowel (CV) unit recognition experiments.

Abstract

This paper proposes a method for automatic detection of breathy voiced vowels in continuous Gujarati speech. As breathy voice is a specific phonetic feature predominantly present in Gujarati among Indian languages, it can be used for identifying Gujarati language. The objective of this paper is to differentiate breathy voiced vowels from modal voiced vowels based on loudness measure. Excitation source characteristics represented by loudness measure are used for differentiating the voice quality. In the proposed method, initially vowel regions in continuous speech are determined by using the knowledge of vowel onset points and epochs. Later, hypothesized vowel segments are classified by using loudness measure. Performance of the proposed method is evaluated on Gujarati speech utterances containing around 47 breathy and 192 modal vowels spoken by 5 male and 5 female speakers. Classification of vowels into breathy or modal voice is achieved with an accuracy of around 94 %.

Abstract

In this paper, the non-uniform duration modification is exploited along with other prosody features for neutral speech to anger speech conversion. The non-uniform duration modification method modifies the durations of vowel and pause segments by different modification factors. Vowel segments are modified by factors based on their identities, and pause segments by uniform factors. Consonant and transition segments are not modified. These modification factors are derived from the analysis of neutral and anger speech. For this purpose, a well known Indian database named as the Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) is chosen for analysis of emotions and synthesis of emotions from neutral speech. The prosodic features used in this study for emotion conversion are pitch contour, intensity contour, and duration contour. Subjective listening test results show that the effectiveness of perception of emotion is better in case of non-uniform duration modification than uniform duration modification

Abstract

In this paper we are proposing neural network based feature transformation framework for developing emotion independent speaker identification system. Most of the present speaker recognition systems may not perform well during emotional environments. In real life, humans extensively express emotions during conversations for effectively conveying the messages. Therefore, in this work we propose the speaker recognition system, robust to variations in emotional moods of speakers. Neural network models are explored to transform the speaker specific spectral features from any specific emotion to neutral. In this work, we have considered eight emotions namely, Anger, Sad, Disgust, Fear, Happy, Neutral, Sarcastic and Surprise. The emotional databases developed in Hindi, Telugu and German are used in this work for analyzing the effect of proposed feature transformation on the performance of speaker identification system. In this work, spectral features are represented by mel-frequency cepstral coefficients, and speaker models are developed using Gaussian mixture models. Performance of the speaker identification system is analyzed with various feature mapping techniques. Results have demonstrated that the proposed neural network based feature transformation has improved the speaker identification performance by 20 %. Feature transformation at the syllable level has shown the better performance, compared to sentence level.