Speech to text conversion and summarization for effective understanding and documentation

Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages. The features of speech differs with each language. However, even while communicating in the same language, the pace and the dialect varies with each person. This creates difficulty in understanding the conveyed message for some people. Sometimes lengthy speeches are also quite difficult to follow due to reasons such as different pronunciation, pace and so on.   Speech recognition which is an inter disciplinary field of computational linguistics aids in developing technologies that empowers the recognition and translation of speech into text. Text summarization extracts the utmost important information from a source which is a text and provides the adequate summary of the same. The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has various applications like lecture notes creation, summarizing catalogues for lengthy documents and so on. Extensive experimentation is performed to validate the efficiency of the proposed metho

Error detection of grapheme-to-phoneme conversion in text-to-speech synthesis using speech signal and lexical context

International audienceIn unit selection text-to-speech synthesis, voice creation involved a phonemic transcription of read speech. This is produced by an automatic grapheme-to-phoneme conversion of the text read, followed by a manual correction. Although grapheme-to-phoneme conversion makes few errors, the manual correction is time consuming as every generated phoneme should be checked. We propose a method to automatically detect grapheme-to-phoneme conversion errors by comparing contrastives phonemisation hypothesis. A lattice-based forced alignment system is implemented, allowing for signal-dependent phonemisation. We implement also a sequence-to-sequence neural network model to obtain a context-dependent grapheme-to-phoneme conversion. On a French dataset, we show that we can detect to 86.3% of the errors made by a commercial grapheme-to-phoneme system. Moreover, the amount of data annotated as erroneous is kept under 10% of the total evaluation data. The time spent for phoneme manual checking can thus been drastically reduced without decreasing significantly the phonemic transcription quality