Towards End-to-end Code-switching Speech Recognition

Abstract

Code-switching speech recognition has attracted an increasing interest recently, but the need for expert linguistic knowledge has always been a big issue. End-to-end automatic speech recognition (ASR) simplifies the building of ASR systems considerably by predicting graphemes or characters directly from acoustic input. In the mean time, the need of expert linguistic knowledge is also eliminated, which makes it an attractive choice for code-switching ASR. This paper presents a hybrid CTC-Attention based end-to-end Mandarin-English code-switching (CS) speech recognition system and studies the effect of hybrid CTC-Attention based models, different modeling units, the inclusion of language identification and different decoding strategies on the task of code-switching ASR. On the SEAME corpus, our system achieves a mixed error rate (MER) of 34.24%.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyluo2018towards

Related papers

• Integrating Knowledge In End-to-end Automatic Speech Recognition For Mandarin-english Code-switching (2021)5.24
• On The End-to-end Solution To Mandarin-english Code-switching Speech Recognition (2018)12.10
• End-to-end Code-switching ASR For Low-resourced Language Pairs (2019)9.76
• Language-agnostic Code-switching In Sequence-to-sequence Speech Recognition (2022)0.00
• The ASRU 2019 Mandarin-english Code-switching Speech Recognition Challenge: Open Datasets, Tracks, Methods And Results (2020)0.00
• Code-switching Speech Recognition Under The Lens: Model- And Data-centric Perspectives (2025)0.00
• An Effective Mixture-of-experts Approach For Code-switching Speech Recognition Leveraging Encoder Disentanglement (2024)0.00
• Rnn-transducer With Language Bias For End-to-end Mandarin-english Code-switching Speech Recognition (2020)8.09