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Speech transcription, emotion recognition, and language identification areusually considered to be three different tasks. Each one requires a differentmodel with a different architecture and training process. We propose using arecurrent neural network transducer (RNN-T)-based speech-to-text (STT) systemas a common component that can be used for emotion recognition and languageidentification as well as for speech recognition. Our work extends the STTsystem for emotion classification through minimal changes, and shows successfulresults on the IEMOCAP and MELD datasets. In addition, we demonstrate that byadding a lightweight component to the RNN-T module, it can also be used forlanguage identification. In our evaluations, this new classifier demonstratesstate-of-the-art accuracy for the NIST-LRE-07 dataset.

Extending RNN-T-based speech recognition systems with emotion and language classification