Typhoon ASR Real-time: FastConformer-Transducer for Thai Automatic Speech Recognition
Large encoder-decoder models like Whisper achieve strong offline transcription but remain impractical for streaming applications due to high latency. However, due to the accessibility of pre-trained checkpoints, the open Thai ASR landscape remains dominated by these offline architectures, leaving a critical gap in efficient streaming solutions. We present Typhoon ASR Real-time, a 115M-parameter FastConformer-Transducer model for low-latency Thai speech recognition. We demonstrate that rigorous text normalization can match the impact of model scaling: our compact model achieves a 45x reduction in computational cost compared to Whisper Large-v3 while delivering comparable accuracy. Our normalization pipeline resolves systemic ambiguities in Thai transcription --including context-dependent number verbalization and repetition markers (mai yamok) --creating consistent training targets. We further introduce a two-stage curriculum learning approach for Isan (north-eastern) dialect adaptation that preserves Central Thai performance. To address reproducibility challenges in Thai ASR, we release the Typhoon ASR Benchmark, a gold-standard human-labeled datasets with transcriptions following established Thai linguistic conventions, providing standardized evaluation protocols for the research community.
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Warit Sirichotedumrong
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2026-01-22T08:47:21.592Z
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Sirichotedumrong
Warit
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Adisai Na-Thalang
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Potsawee Manakul
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2026-02-04T19:04:14.363Z
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Potsawee Manakul
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Pittawat Taveekitworachai
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2026-01-22T08:47:23.874Z
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Pittawat Taveekitworachai
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Sittipong Sripaisarnmongkol
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Kunat Pipatanakul
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2026-01-26T08:33:43.323Z
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Kunat Pipatanakul
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kunato
Typhoon ASR Real-time: FastConformer-Transducer for Thai Automatic Speech Recognition
Large encoder-decoder models like Whisper achieve strong offline transcription but remain impractical for streaming applications due to high latency. However, due to the accessibility of pre-trained checkpoints, the open Thai ASR landscape remains dominated by these offline architectures, leaving a critical gap in efficient streaming solutions. We present Typhoon ASR Real-time, a 115M-parameter FastConformer-Transducer model for low-latency Thai speech recognition. We demonstrate that rigorous text normalization can match the impact of model scaling: our compact model achieves a 45x reduction in computational cost compared to Whisper Large-v3 while delivering comparable accuracy. Our normalization pipeline resolves systemic ambiguities in Thai transcription --including context-dependent number verbalization and repetition markers (mai yamok) --creating consistent training targets. We further introduce a two-stage curriculum learning approach for Isan (north-eastern) dialect adaptation that preserves Central Thai performance. To address reproducibility challenges in Thai ASR, we release the Typhoon ASR Benchmark, a gold-standard human-labeled datasets with transcriptions following established Thai linguistic conventions, providing standardized evaluation protocols for the research community.
A 115M-parameter FastConformer-Transducer model achieves low-latency Thai speech recognition with reduced computational cost through text normalization and curriculum learning, accompanied by a benchmark dataset for standardized evaluation.
FastConformer-Transducer streaming applications text normalization curriculum learning Thai ASR speech recognition computational cost model scaling linguistic conventions benchmark dataset