System

A system comprising:

• a communication interface including a network interface controller coupled to a packet-switched network and configured to transmit and receive data packets;
• a memory storing instructions, a speech recognition model, a generative neural network model comprising a transformer architecture with an encoder and a decoder, dictionary information mapping variant-specific vocabulary to standard language vocabulary, and acoustic and prosodic feature data;
• and
• circuitry comprising one or more processors coupled to the memory and configured to execute the instructions to:
• receive, via the communication interface, audio data representing speech in a regional language variant from one or more terminal devices;
• perform signal processing on the audio data including noise reduction, silent segment removal, and sound level normalization to generate preprocessed audio data;
• calculate acoustic features including mel-frequency cepstral coefficients and prosodic features including pitch patterns from the preprocessed audio data;
• input the preprocessed audio data to the speech recognition model to obtain text data, extract variant-specific vocabulary, and update the dictionary information;
• generate training data combining the dictionary information, the acoustic features, the prosodic features, and corresponding text data, and train the generative neural network model to perform conversion between variant expressions and standard language expressions;
• evaluate the trained generative neural network model by computing word recognition rate and variant expression conversion accuracy against reference data;
• and
• expand support to additional regional language variants by generating variant-specific training data and training additional generative neural network models.

A method performed by circuitry of a server coupled to a packet-switched network via a communication interface, the method comprising:

• receiving, via the communication interface, audio data representing speech in a regional language variant from one or more terminal devices;
• performing preprocessing on the audio data including noise removal and converting the audio data into text data;
• training a generative neural network model to learn variant-specific pronunciation and intonation based on the preprocessed audio data and corresponding text data;
• evaluating an accuracy of speech recognition using the trained generative neural network model;
• and
• expanding support for speech recognition from a first regional language variant to additional regional language variants.