Q: what is the domain of the corpus?
Text: Introduction
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 ).
From unsupervised term discovery to direct speech-to-text translation
For UTD we use the Zero Resource Toolkit (ZRTools; Jansen and Van Durme, 2011). ZRTools uses dynamic time warping (DTW) to discover pairs of acoustically similar audio segments, and then uses graph clustering on overlapping pairs to form a hard clustering of the discovered segments. Replacing each discovered segment with its unique cluster label, or pseudoterm, gives us a partial, noisy transcription, or pseudotext (Fig. FIGREF4 ).
In creating a translation model from this data, we face a difficulty that does not arise in the parallel texts that are normally used to train translation models: the pseudotext does not represent all of the source words, since the discovered segments do not cover the full audio (Fig. FIGREF4 ). Hence we must not assume that our MT model can completely recover the translation of a test sentence. In these conditions, the language modeling and ordering assumptions of most MT models are unwarranted, so we instead use a simple bag-of-words translation model based only on co-occurrence: IBM Model 1 BIBREF11 with a Dirichlet prior over translation distributions, as learned by fast_align BIBREF12 . In particular, for each pseudoterm, we learn a translation distribution over possible target words. To translate a pseudoterm in test data, we simply return its highest-probability translation (or translations, as discussed in § SECREF5 ).
This setup implies that in order to translate, we must apply UTD on both the training and test audio. Using additional (not only training) audio in UTD increases the likelihood of discovering more clusters. We therefore generate pseudotext for the combined audio, train the MT model on the pseudotext of the training audio, and apply it to the pseudotext of the test data. This is fair since the UTD has access to only the audio.
Dataset
Although we did not have access to a low-resource dataset, there is a corpus of noisy multi-speaker speech that simulates many of the conditions we expect to find in our motivating applications: the CALLHOME Spanish–English speech translation dataset (LDC2014T23; Post el al., 2013). We ran UTD over all 104 telephone calls, which pair 11 hours of audio with Spanish transcripts and their crowdsourced English translations. The transcripts contain 168,195 Spanish word tokens (10,674 types), and the translations contain 159,777 English word tokens (6,723 types). Though our system does not require Spanish transcripts, we use them to evaluate UTD and to simulate a perfect UTD system, called the oracle.
For MT training, we use the pseudotext and translations of 50 calls, and we filter out stopwords in the translations with NLTK BIBREF15 . Since UTD is better at matching patterns from the same speaker (§ SECREF8 ), we created two types of 90/10% train/test split: at the call level and at the utterance level. For the latter, 90% of the utterances are randomly chosen for the training set (independent of which call they occur in), and the rest go in the test set. Hence at the utterance level, but not the call level, some speakers are included in both training and test data. Although the utterance-level split is optimistic, it allows us to investigate how multiple speakers affect system performance. In either case, the oracle has about 38k Spanish tokens to train on.
Analysis of challenges from UTD
Our system relies on the pseudotext produced by ZRTools (the only freely available UTD system we are aware of), which presents several challenges for MT. We used the default ZRTools parameters, and it might be possible to tune them to our task, but we leave this to future work.
Assigning wrong words to a cluster
Since UTD is unsupervised, the discovered clusters are noisy. Fig. FIGREF4 shows an example of an incorrect match between the acoustically similar “qué tal vas con” and “te trabajo y” in utterances B and C, leading to a common assignment to c2. Such inconsistencies in turn affect the translation distribution conditioned on c2.
Many of these errors are due to cross-speaker matches, which are known to be more challenging for UTD BIBREF16 , BIBREF17 , BIBREF18 . Most matches in our corpus are across calls, yet these are also the least accurate (Table TABREF9 ). Within-utterance matches, which are always from the same speaker, are the most reliable, but make up the smallest proportion of the discovered pairs. Within-call matches fall in between. Overall, average cluster purity is only 34%, meaning that 66% of discovered patterns do not match the most frequent type in their cluster.
Splitting words across different clusters
Although most UTD matches are across speakers, recall of cross-speaker matches is lower than for same-speaker matches. As a result, the same word from different speakers often appears in multiple clusters, preventing the model from learning good translations. ZRTools discovers 15,089 clusters in our data, though there are only 10,674 word types. Only 1,614 of the clusters map one-to-one to a unique word type, while a many-to-one mapping of the rest covers only 1,819 gold types (leaving 7,241 gold types with no corresponding cluster).
Fragmentation of words across clusters renders pseudoterms impossible to translate when they appear only in test and not in training. Table TABREF10 shows that these pseudotext out-of-vocabulary (OOV) words are frequent, especially in the call-level split. This reflects differences in acoustic patterns of different speakers, but also in their vocabulary — even the oracle OOV rate is higher in the call-level split.
UTD is sparse, giving low coverage
UTD is most reliable on long and frequently-repeated patterns, so many spoken words are not represented in the pseudotext, as in Fig. FIGREF4 . We found that the patterns discovered by ZRTools match only 28% of the audio. This low coverage reduces training data size, affects alignment quality, and adversely affects translation, which is only possible when pseudoterms are present. For almost half the utterances, UTD fails to produce any pseudoterm at all.
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