Grapheme-to-Phoneme Conversion
16 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Grapheme-to-Phoneme Conversion
Most implemented papers
Deep Voice: Real-time Neural Text-to-Speech
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks.
Applying the Transformer to Character-level Transduction
The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks.
Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models
We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion.
Massively Multilingual Neural Grapheme-to-Phoneme Conversion
Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems.
g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset
Conversion of Chinese graphemes to phonemes (G2P) is an essential component in Mandarin Chinese Text-To-Speech (TTS) systems.
Transformer based Grapheme-to-Phoneme Conversion
The transformer network architecture is completely based on attention mechanisms, and it outperforms sequence-to-sequence models in neural machine translation without recurrent and convolutional layers.
Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi
Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted).
Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies
Here, we propose a model that does not: a pointer-generator transformer for disjoint vocabularies.
Smoothing and Shrinking the Sparse Seq2Seq Search Space
Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.
On Biasing Transformer Attention Towards Monotonicity
Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining.