Project Overview Goal: Develop a system capable of converting text to speech and vice versa, enabling seamless communication between humans and machines. Objectives: $$ Text$-$to$-$Speech $($TTS$)$: o Create a TTS engine that can convert text into natural$-$sounding speech. o Experiment with di@erent synthesis techniques $($e$.$g$.,$ concatenative synthesis, formant synthesis$)$ to achieve high$-$quality output. o Consider factors like intonation, pitch, and speaking rate to make the synthesized speech more human$-$like. $$ Speech$-$to$-$Text $($STT$)$: o Develop an STT system that can accurately transcribe spoken language into text. o Explore various acoustic modeling and language modeling techniques $($e$.$g$.,$ Hidden Markov Models, Neural Networks$)$ to improve accuracy. o Handle challenges like accents, dialects, and background noise. System Architecture $$ Text$-$to$-$Speech $($TTS$)$: o Text normalization: Convert text to a canonical form $($e$.$g$.,$ handling contractions, abbreviations$).$ o Text$-$to$-$phoneme conversion: Convert text into a sequence of phonemes. o Prosody modeling: Determine the appropriate intonation, pitch, and speaking rate for each phoneme. o Synthesis: Generate speech waveforms using synthesis techniques. $$ Speech$-$to$-$Text $($STT$)$: o Feature extraction: Extract acoustic features from the speech signal $($e$.$g$.,$ Mel$-$frequency cepstral coe@icients, delta coe@icients$).$ o Acoustic modeling: Model the relationship between acoustic features and phonemes using techniques like Hidden Markov Models or Deep Neural Networks. o Language modeling: Predict the most likely sequence of words given the acoustic features and language context. o Decoding: Combine the acoustic and language models to produce the transcribed text. Evaluation Metrics $$ TTS: o Naturalness: Subjective evaluation by human listeners. o Intelligibility: Objective measures like word error rate or phoneme error rate. o Quality: Technical metrics like signal$-$to$-$noise ratio and distortion. $$ STT: o Word error rate $($WER$)$: Measures the percentage of words incorrectly transcribed. o Character error rate $($CER$)$: Measures the percentage of characters incorrectly transcribed. o Sentence error rate $($SER$)$: Measures the percentage of sentences incorrectly transcribed.