Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning

• URL: http://arxiv.org/abs/2403.15469v1
• Date: Wed, 20 Mar 2024 08:52:40 GMT
• Title: Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning
• Authors: Shivam Ratnakant Mhaskar, Nirmesh J. Shah, Mohammadi Zaki, Ashishkumar P. Gudmalwar, Pankaj Wasnik, Rajiv Ratn Shah,
• Abstract summary: In this paper, we present the development of an isometric NMT system using Reinforcement Learning (RL) To evaluate our models, we propose the Phoneme Count Compliance (PCC) score, which is a measure of length compliance. Our approach demonstrates a substantial improvement of approximately 36% in the PCC score compared to the state-of-the-art models when applied to English-Hindi language pairs.
• Score: 31.26989690734889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional Automatic Video Dubbing (AVD) pipeline consists of three key modules, namely, Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), and Text-to-Speech (TTS). Within AVD pipelines, isometric-NMT algorithms are employed to regulate the length of the synthesized output text. This is done to guarantee synchronization with respect to the alignment of video and audio subsequent to the dubbing process. Previous approaches have focused on aligning the number of characters and words in the source and target language texts of Machine Translation models. However, our approach aims to align the number of phonemes instead, as they are closely associated with speech duration. In this paper, we present the development of an isometric NMT system using Reinforcement Learning (RL), with a focus on optimizing the alignment of phoneme counts in the source and target language sentence pairs. To evaluate our models, we propose the Phoneme Count Compliance (PCC) score, which is a measure of length compliance. Our approach demonstrates a substantial improvement of approximately 36% in the PCC score compared to the state-of-the-art models when applied to English-Hindi language pairs. Moreover, we propose a student-teacher architecture within the framework of our RL approach to maintain a trade-off between the phoneme count and translation quality.

Related papers

• Lina-Speech: Gated Linear Attention is a Fast and Parameter-Efficient Learner for text-to-speech synthesis [7.2129341612013285]
  We introduce Lina-Speech, a model that replaces traditional self-attention mechanisms with emerging recurrent architectures like Gated Linear Attention (GLA) This approach is fast, easy to deploy, and achieves performance comparable to fine-tuned baselines when the dataset size ranges from 3 to 15 minutes.
  arXiv  Detail & Related papers  (2024-10-30T04:50:40Z)
• C3LLM: Conditional Multimodal Content Generation Using Large Language Models [66.11184017840688]
  We introduce C3LLM, a novel framework combining three tasks of video-to-audio, audio-to-text, and text-to-audio together. C3LLM adapts the Large Language Model (LLM) structure as a bridge for aligning different modalities. Our method combines the previous tasks of audio understanding, video-to-audio generation, and text-to-audio generation together into one unified model.
  arXiv  Detail & Related papers  (2024-05-25T09:10:12Z)
• TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer [3.9981390090442694]
  We present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems.
  arXiv  Detail & Related papers  (2024-05-03T14:25:21Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset [53.46019570679092]
  We propose a Vision-Audio-Language Omni-peRception pretraining model (VALOR) for multi-modal understanding and generation. VALOR jointly models relationships of vision, audio and language in an end-to-end manner. It achieves new state-of-the-art performances on series of public cross-modality benchmarks.
  arXiv  Detail & Related papers  (2023-04-17T15:08:15Z)
• Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding [55.989376102986654]
  This paper studies a transferable phoneme embedding framework that aims to deal with the cross-lingual text-to-speech problem under the few-shot setting. We propose a framework that consists of a phoneme-based TTS model and a codebook module to project phonemes from different languages into a learned latent space.
  arXiv  Detail & Related papers  (2022-06-27T11:24:40Z)
• Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE [36.50265124324876]
  We propose a novel unsupervised text-to-speech acoustic model training scheme, named UTTS, which does not require text-audio pairs. The framework offers a flexible choice of a speaker's duration model, timbre feature (identity) and content for TTS inference. Experiments demonstrate that UTTS can synthesize speech of high naturalness and intelligibility measured by human and objective evaluations.
  arXiv  Detail & Related papers  (2022-06-06T11:51:22Z)
• IsometricMT: Neural Machine Translation for Automatic Dubbing [9.605781943224251]
  This work introduces a self-learning approach that allows a transformer model to directly learn to generate outputs that closely match the source length. We report results on four language pairs with a publicly available benchmark based on TED Talk data.
  arXiv  Detail & Related papers  (2021-12-16T08:03:20Z)
• You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation [59.31769998728787]
  We build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.
  arXiv  Detail & Related papers  (2020-05-14T17:24:57Z)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.