Producing human voice speech from datasets typically involves using techniques from speech synthesis or text-to-speech (TTS) systems. Here are some general steps that are typically followed in the process:
The first step is gathering suitable speech recordings or text datasets. The dataset should represent the types of speech that the TTS system will produce and be of high quality. The dataset may need to be preprocessed to remove noise or other artifacts.
The next step is to extract features from the speech dataset that can be used as input to the TTS system. Standard features include pitch, spectral envelope, and prosodic features such as intonation and stress.
If the TTS system is designed to generate speech from text input, the text must be analyzed to determine the appropriate prosody and pronunciation for each word or phrase. This typically involves using a natural language processing (NLP) system to parse the text and extract relevant features such as part-of-speech tags and named entities.
The TTS system then uses the extracted features and text analysis to model the relationship between text input and speech output. This involves training a statistical or machine learning model, such as a neural network, to predict the appropriate acoustic features for a given text input.
Once the TTS model is trained, it can synthesize speech from new text inputs. The model takes the input text and generates a sequence of acoustic features to produce speech output. The acoustic features are typically converted to a waveform using a vocoder or other signal-processing techniques.
The synthesized speech is evaluated to ensure it meets the desired quality criteria. This may involve subjective evaluation by human listeners or objective evaluation using metrics such as speech intelligibility or naturalness.
The techniques and algorithms used in each process step may vary depending on the specific TTS system used and the target speech’s characteristics.