Punjabi Speech Synthesis System Using Htk

This paper describes an Hidden Markov Model-based Punjabi text-to-speech synthesis system (HTS), in which speech waveform is generated from Hidden Markov Models themselves, and applies it to Punjabi speech synthesis using the general speech synthesis architecture of HTK (HMM Tool Kit). This Hidden Markov Model based TTS can be used in mobile phones for stored phone directory or messages. Text messages and caller’s identity in English language are mapped to tokens in Punjabi language which are further concatenated to form speech with certain rules and procedures. To build the synthesizer we recorded the speech database and phonetically segmented it, thus first extracting context-independent monophones and then context-dependent triphones. For e.g. for word bharat monophones are a, bh, t etc. & triphones are bh-a+r. These speech utterances and their phone level transcriptions (monophones and triphones) are the inputs to the speech synthesis system. System outputs the sequence of phonemes after resolving various ambiguities regarding selection of phonemes using word network files e.g. for the word Tapas the output phoneme sequence is ਤ,ਪ,ਸ instead of phoneme sequence ਟ,ਪ,ਸ .