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Incorporation of speech and Indian scripts can greatly enhance the accessibility of web information among common people. This paper describes a 'web reader' which 'reads out' the textual contents of a selected web page in Hindi or in English with Indian accent. The content of the page is downloaded and parsed into suitable textual form. It is then passed on to an indigenously developed text-to-speech system for Hindi/Indian English, to generate spoken output. The text-to-speech conversion is performed in three stages: text analysis, to establish pronunciation, phoneme to acoustic-phonetic parameter conversion and, lastly, parameter-to-speech conversion through a production model. Different types of voices are used to read special messages. The web reader detects the hypertext links in the web pages and gives the user the option to follow the link or continue perusing the current web page. The user can exercise the option either through a keyboard or via spoken commands. Future plans include refining the web parser, improvement of naturalness of synthetic speech and improving the robustness of the speech recognition system.

This paper presents a description of a speech recognition system for Hindi. The system follows a hierarchic approach to speech recognition and integrates multiple knowledge sources within statistical pattern recognition paradigms at various stages of signal decoding. Rather than make hard decisions at the level of each processing unit, relative confidence scores of individual units are propagated to higher levels. Phoneme recognition is achieved in two stages: broad acoustic classification of a frame is followed by fine acoustic classification. A semi-Markov model processes the frame level outputs of a broad acoustic maximum likelihood classifier to yield a sequence of segments with broad acoustic labels. The phonemic identities of selected classes of segments are decoded by class-dependent neural nets which are trained with class-specific feature vectors as input. Lexical access is achieved by string matching using a dynamic programming technique. A novel language processor disambiguates between multiple choices given by the acoustic recognizer to recognize the spoken sentence.

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