Connectionist Temporal Classification (CTC) is a widely used approach for automatic
speech recognition (ASR) that performs conditionally independent monotonic alignment …

In this paper, we study articulatory synthesis, a speech synthesis method using human vocal
tract information that offers a way to develop efficient, generalizable and interpretable …

Simultaneous speech translation (SimulST) is a demanding task that involves generating
translations in real-time while continuously processing speech input. This paper offers a …

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the
broadening interests of the spoken language translation community. ESPnet-ST-v2 supports …

End-to-end speech summarization has been shown to improve performance over cascade
baselines. However, such models are difficult to train on very large inputs (dozens of …

Blockwise self-attentional encoder models have recently emerged as one promising end-to-
end approach to simultaneous speech translation. These models employ a blockwise beam …

Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great
success by jointly learning acoustic and linguistic information, it still suffers from the effect of …

Simultaneous speech-to-text translation (SimulST) translates source-language speech into
target-language text concurrently with the speaker's speech, ensuring low latency for better …

Contemporary datasets provide an oracle segmentation into sentences based on human-
annotated transcripts and translations. However, the segmentation into sentences is not …

Conventional speech-to-text translation (ST) systems are trained on single-speaker
utterances, and they may not generalize to real-life scenarios where the audio contains …