Phoneme recognition in TIMIT with BLSTM-CTC

A BLSTM Network for Printed Bengali OCR System with High Accuracy

This paper presents a printed Bengali and English text OCR system developed by us using a single hidden BLSTM-CTC architecture having 128 units. Here, we did not use any peephole connection and dropout in the BLSTM, which helped us in getting better accuracy. This architecture was trained by 47,720 text lines that include English words also. When tested over 20 different Bengali fonts, it has produced character level accuracy of 99.32% and word level accuracy of 96.65%. A good Indic multi script OCR system is also developed by Google. It sometimes recognizes a character of Bengali into the sam…

利用标签先验实现更平滑、更准确的 CTC 强制对齐

Less Peaky and More Accurate CTC Forced Alignment by Label Priors

本文利用标签先验改进 CTC 强制对齐，缓解峰值分布，提升音素和词边界预测精度，在 Buckeye 和 TIMIT 上优于标准 CTC，且训练简单高效。

A Study on Effects of Implicit and Explicit Language Model Information for DBLSTM-CTC Based Handwriting Recognition

Deep Bidirectional Long Short-Term Memory (D-BLSTM) with a Connectionist Temporal Classification (CTC) output layer has been established as one of the state-of-the-art solutions for handwriting recognition. It is well known that the DBLSTM trained by using a CTC objective function will learn both local character image dependency for character modeling and long-range contextual dependency for implicit language modeling. In this paper, we study the effects of implicit and explicit language model information for DBLSTM-CTC based handwriting recognition by comparing the performance of using or wit…

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the state-of-the-art in various benchmarks. Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid setting…

TIPAA-SSL：基于自监督学习与知识迁移的文本无关音素到音频对齐

TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer

本文提出TIPAA-SSL方法，通过微调wav2vec2进行音素识别并结合降维与帧级分类器实现文本无关音素到音频对齐，在TIMIT和SCRIBE数据集上优于charsiu。