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Introduction
Warthin's tumor (WT) is a common benign salivary gland neoplasm with a negligible risk of malignant transformation. However, there is a risk of malignant tumors being misdiagnosed as WT on cytology and inappropriately managed conservatively.
Methods
Patients from nine centers in Italy and the United Kingdom undergoing parotid surgery for cytologically diagnosed WT were included in...
We train grapheme-based acoustic models for speech recognition using a hierarchical recurrent neural network architecture with connectionist temporal classification (CTC) loss. The models learn to align utterances with phonetic transcriptions in a lower layer and graphemic transcriptions in the final layer in a multi-task learning setting. Using the grapheme predictions from a hierarchical model trained...
We present a recipe for training acoustic models with context dependent (CD) phones from scratch using recurrent neural networks (RNNs). First, we use the connectionist temporal classification (CTC) technique to train a model with context independent (CI) phones directly from the written-domain word transcripts by aligning with all possible phonetic verbalizations. Then, we devise a mechanism to generate...
We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its...
This paper describes a series of experiments to extend the application of Context-Dependent (CD) long short-term memory (LSTM) recurrent neural networks (RNNs) trained with Connectionist Temporal Classification (CTC) and sMBR loss. Our experiments, on a noisy, reverberant voice search task, include training with alternative pronunciations and the application to child speech recognition; combination...
Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced. We propose a G2P model based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN). In contrast to traditional joint-sequence based G2P approaches, LSTMs have the flexibility of taking into consideration the full context of graphemes and transform...
Pronunciations for words are a critical component in an automated speech recognition system (ASR) as mis-recognitions may be caused by missing or inaccurate pronunciations. The need for high quality pronunciations has recently motivated data-driven techniques to generate them [1]. We propose a data-driven and language-independent framework for verification of such pronunciations to further improve...
We explore alternative acoustic modeling techniques for large vocabulary speech recognition using Long Short-Term Memory recurrent neural networks. For an acoustic frame labeling task, we compare the conventional approach of cross-entropy (CE) training using fixed forced-alignments of frames and labels, with the Connectionist Temporal Classification (CTC) method proposed for labeling unsegmented sequence...
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