End-to-end attention-based large vocabulary speech recognition

Abstract

Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches.

Character-level incremental speech recognition with recurrent neural networks

“…The word "ROCK" is corrected to "DRAW" after hearing "RATE" and "IN DRAW RATE" to "AND DRAW CROWD" while hearing "PEOPLE". [9] CTC + Trigram (extended) 7.34% Miao et al [9] CTC + Trigram 9.07% Hannun et al [8] CTC + Bigram 14.1% Bahdanau et al [10] Encoder-decoder + Trigram 11.3% Woodland et al [21] GMM-HMM + Trigram 9.46% Miao et al [9] DNN-HMM + Trigram 7.14% is roughly 0.5% to 1% WER. However, there was little difference when the beam width increases from 512 to 2048 in our preliminary experiments.…”

“…Also, a sub-lexical language model is proposed in [5] for detecting previously unseen words. RNN-based character-level end-to-end ASR systems were studied in [6,7,8,9,10]. However, they lack the capability of dictating OOV words since the decoding is performed with word-level LMs.…”

HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks

“…In this sense, this task is similar to aspect-based sentiment analysis (Pontiki et al, 2016), where the task is not to classify a text or sentence, but an entity within the text. The notion of focus is similar to attention (Bahdanau et al, 2016;Yin et al, 2016), with the difference that attention is learned during training whereas focus is given as an additional input.…”

Sentiment analysis of feature ranking methods for classification accuracy

“…The N-grams of phrases has dimensions as 1 dimensional, 2 dimensional, 3 dimensional and they called as 'unigrams', 'bigrams', 'trigrams' respectively. The N-Grams are mostly using for the speech pattern recognition [14,15] and identify the particular language. Also text classification requires the N-Grams for effective classification [16].…”