We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language, nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.
Early work on (ST) @cite_21 -- translating audio in one language into text in another -- used lattices from an ASR system as inputs to translation models @cite_29 @cite_20 , giving the translation model access to the speech recognition uncertainty. Alternative approaches explicitly integrated acoustic and translation models using a stochastic finite-state transducer which can decode the translated text directly using Viterbi search @cite_24 @cite_13 .
@cite_29 @cite_21 @cite_24 @cite_13 @cite_20
399167303 2952010730 2139647714 2952465655
We propose a novel technique for adapting text-based statistical machine translation to deal with input from automatic speech recognition in spoken language translation tasks. We simulate likely misrecognition errors using only a source language pronunciation dictionary and language model (i.e., without an acoustic model), and use these to augment the phrase table of a standard MT system. The augmented system can thus recover from recognition errors during decoding using synthesized phrases. Using the outputs of five different English ASR systems as input, we find consistent and significant improvements in translation quality. Our proposed technique can also be used in conjunction with lattices as ASR output, leading to further improvements. In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden states extracted by a pre-trained E2E-ASR encoder from a sequence of characters. By using hidden states as a target instead of acoustic features, it is possible to achieve faster attention learning and reduce computational cost, thanks to sub-sampling in E2E-ASR encoder, also the use of the hidden states can avoid to model speaker dependencies unlike acoustic features. After training, the text-to-encoder model generates the hidden states from a large amount of unpaired text, then E2E-ASR decoder is retrained using the generated hidden states as additional training data. Experimental evaluation using LibriSpeech dataset demonstrates that our proposed method achieves improvement of ASR performance and reduces the number of unknown words without the need for paired data. A fully integrated approach to speech input language translation in limited domain applications is presented. The mapping from the input to the output language is modeled in terms of a finite state translation model which is learned from examples of input output sentences of the task considered. This model is tightly integrated with standard acoustic phonetic models of the input language and the resulting global model directly supplies, through Viterbi search, an optimal output language sentence for each input language utterance. Several extensions to this framework, recently developed to cope with the increasing difficulty of translation tasks, are reviewed. Finally, results for a task in the framework of hotel front desk communication, with a vocabulary of about 700 words, are reported. We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.