Question: What are the limitations of ASR and MT?
Evidence: Typical speech-to-text translation systems pipeline automatic speech recognition (ASR) and machine translation (MT) BIBREF0 . But high-quality ASR requires hundreds of hours of transcribed audio, while high-quality MT requires millions of words of parallel text—resources available for only a tiny fraction of the world's estimated 7,000 languages BIBREF1 . Nevertheless, there are important low-resource settings in which even limited speech translation would be of immense value: documentation of endangered languages, which often have no writing system BIBREF2 , BIBREF3 ; and crisis response, for which text applications have proven useful BIBREF4 , but only help literate populations. In these settings, target translations may be available. For example, ad hoc translations may be collected in support of relief operations. Can we do anything at all with this data?
In this exploratory study, we present a speech-to-text translation system that learns directly from source audio and target text pairs, and does not require intermediate ASR or MT. Our work complements several lines of related recent work. For example, duong2015attentional and antonios+chiang+duongEMNLP2016 presented models that align audio to translated text, but neither used these models to try to translate new utterances (in fact, the latter model cannot make such predictions). berard+etalnipsworkshop16 did develop a direct speech to translation system, but presented results only on a corpus of synthetic audio with a small number of speakers. Finally, Adams et al. adams+etalinterspeech16,adams+etalemnlp16 targeted the same low-resource speech-to-translation task, but instead of working with audio, they started from word or phoneme lattices. In principle these could be produced in an unsupervised or minimally-supervised way, but in practice they used supervised ASR/phone recognition. Additionally, their evaluation focused on phone error rate rather than translation. In contrast to these approaches, our method can make translation predictions for audio input not seen during training, and we evaluate it on real multi-speaker speech data.
Our simple system (§ SECREF2 ) builds on unsupervised speech processing BIBREF5 , BIBREF6 , BIBREF7 , and in particular on unsupervised term discovery (UTD), which creates hard clusters of repeated word-like units in raw speech BIBREF8 , BIBREF9 . The clusters do not account for all of the audio, but we can use them to simulate a partial, noisy transcription, or pseudotext, which we pair with translations to learn a bag-of-words translation model. We test our system on the CALLHOME Spanish-English speech translation corpus BIBREF10 , a noisy multi-speaker corpus of telephone calls in a variety of Spanish dialects (§ SECREF3 ). Using the Spanish speech as the source and English text translations as the target, we identify several challenges in the use of UTD, including low coverage of audio and difficulty in cross-speaker clustering (§ SECREF4 ). Despite these difficulties, we demonstrate that the system learns to translate some content words (§ SECREF5 ).
But high-quality ASR requires hundreds of hours of transcribed audio, while high-quality MT requires millions of words of parallel text—resources available for only a tiny fraction of the world’s estimated 7,000 languages (Besacier et al., 2014).
Answer: High-quality ASR requires hundreds of hours of transcribed audio, while high-quality MT requires millions of words of parallel text—resources available for only a tiny fraction of the world’s estimated 7,000 languages.