Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections, furthermore, a model, which utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate

Structure Learning in Hidden Conditional Random Fields for Grapheme-to-Phoneme Conversion

Accurate grapheme-to-phoneme (g2p) conversion is needed for several speech processing applications, such as automatic speech synthesis and recognition. For some languages, notably English, improvements of g2p systems are very slow, due to the intricacy of the associations between letter and sounds. In recent years, several improvements have been obtained either by using variable-length associations in generative models (jointn-grams), or by recasting the problem as a conventional sequence labeling task, enabling to integrate rich dependencies in discriminative models. In this paper, we consider several ways to reconciliate these two approaches. Introducing hidden variable-length alignments through latent variables, our Hidden Conditional Random Field (HCRF) models are able to produce comparative performance compared to strong generative and discriminative models on the CELEX database