Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation (1612.01744v1)

Abstract: This paper proposes a first attempt to build an end-to-end speech-to-text translation system, which does not use source language transcription during learning or decoding. We propose a model for direct speech-to-text translation, which gives promising results on a small French-English synthetic corpus. Relaxing the need for source language transcription would drastically change the data collection methodology in speech translation, especially in under-resourced scenarios. For instance, in the former project DARPA TRANSTAC (speech translation from spoken Arabic dialects), a large effort was devoted to the collection of speech transcripts (and a prerequisite to obtain transcripts was often a detailed transcription guide for languages with little standardized spelling). Now, if end-to-end approaches for speech-to-text translation are successful, one might consider collecting data by asking bilingual speakers to directly utter speech in the source language from target language text utterances. Such an approach has the advantage to be applicable to any unwritten (source) language.

Overview of "Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation"

The paper titled "Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation" presents a novel approach in the domain of speech translation by proposing an end-to-end system that bypasses the traditional requirement for intermediate text transcription of the source language. By eliminating the dependency on source language text, this approach has the potential to transform the data collection methodology, particularly in scenarios involving under-resourced languages.

Methodology and Model Architecture

The proposed models leverage the encoder-decoder architecture, which is inherently capable of performing complex sequence-to-sequence tasks. In this setup, two primary models are implemented: a standard machine translation model and a novel speech translation model. Both models utilize bidirectional LSTMs in the encoder and employ an attention mechanism to manage the input sequences.

1. Attention Mechanism: The text translation model utilizes a standard soft attention model, while the speech translation model employs a convolutional attention mechanism. The convolutional attention, as used in speech processing, acknowledges the previous attention weights and incorporates a convolution filter to mitigate redundancy in translating the same input signal multiple times—an adaptation particularly crucial because of the elongated nature of speech input sequences.
2. Implementation Details: The models are built upon the seq2seq framework with enhancements such as a beam-search decoder, multi-task training, hierarchical encoding for the speech model, and the application of dropout to prevent overfitting during model training. The unique applications, such as convolutional attention and hierarchical encoding for the speech translation model, underscore the paper's methodological contributions.

Experiments and Results

The researchers performed comprehensive experiments involving synthetic corpora created from the BTEC dataset. The text translation experiments established a comparative baseline using a neural machine translation (NMT) model that surprisingly matched the BLEU scores of a baseline phrase-based SMT system, considering the dataset's limited size.

For speech translation, the system's performance was benchmarked against a conventional pipeline involving speech recognition followed by machine translation, with competitive results. Notably, the model exhibited the capability to generalize to new speakers, indicating robustness against inter-speaker variability, albeit within the confines of synthetic data.

Key Numerical Results:

• On the speech translation task, the BLEU scores achieved were promisingly close, though not surpassing those of the baseline systems using human inputs.
• The introduction of ensemble methods improved BLEU performance significantly, highlighting their utility in enhancing the end-to-end system's translation accuracy.

Theoretical and Practical Implications

The elimination of textual intermediaries in the translation pipeline propels forward the applicability of such models, especially in linguistically diverse areas lacking formal writing systems. This research opens up new avenues for deploying deep learning models in multilingual settings, effectively broadening the scope of language technologies.

Future Directions

Future work should focus on scaling these models with non-synthetic datasets to rigor the end-to-end approach's performance in real-world applications. Additionally, exploring multi-source translation settings could further refine the utility of this model by integrating multiple modalities of input data. Investigations into datasets such as TED Talks or projects like Project Gutenberg could offer rich grounds for future research endeavors in expanding end-to-end speech translation models across varying domains and contexts.

The paper sets a foundational precursor to more extensive studies about removing conventional barriers found in language translation, driving forward the domain of autonomous multilingual communication systems.