Merghani-SACNAS Poster
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This document presents research on developing an automatic speech recognition system for Egyptian Arabic. It explores using a diacritized lexicon versus an undiacritized graphemic lexicon, and compares different acoustic models. The researchers used the Al-Jazeera Egyptian dialect corpus and evaluated performance using word error rate. Their results showed that a graphemic lexicon outperformed a diacritized lexicon, and that reducing out-of-vocabulary words and using deep neural network acoustic models improved word error rate.
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improve the ASR performance for Myanmar language by changing different Convolutional Neural Network
(CNN) hyperparameters such as number of feature maps and pooling size. CNN has the abilities of
reducing in spectral variations and modeling spectral correlations that exist in the signal due to the locality
and pooling operation. Therefore, the impact of the hyperparameters on CNN accuracy in ASR tasks is
investigated. A 42-hr-data set is used as training data and the ASR performance was evaluated on two open
test sets: web news and recorded data. As Myanmar language is a syllable-timed language, ASR based on
syllable was built and compared with ASR based on word. As the result, it gained 16.7% word error rate
(WER) and 11.5% syllable error rate (SER) on TestSet1. And it also achieved 21.83% WER and 15.76%
SER on TestSet2.
Wreck a nice beach: adventures in speech recognition
Introduction to speech recognition and a description of a project to integrate CMU Sphinx into the Opencast Matterhorn lecture capture system, focusing on language model adaptation using Wikipedia as a corpus.
ATAR: Attention-based LSTM for Arabizi transliteration
A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present ATAR, an ATtention-based LSTM model for ARabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).
Lenar Gabdrakhmanov (Provectus): Speech synthesis
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
This document summarizes a tutorial for developing a state-of-the-art named entity recognition framework using deep learning. The tutorial uses a bi-directional LSTM-CNN architecture with a CRF layer, as presented in a 2016 paper. It replicates the paper's results on the CoNLL 2003 dataset for NER, achieving an F1 score of 91.21. The tutorial covers data preparation from the dataset, word embeddings using GloVe vectors, a CNN encoder for character-level representations, a bi-LSTM for word-level encoding, and a CRF layer for output decoding and sequence tagging. The experience of presenting this tutorial to friends highlighted the need for detailed comments and explanations of each step and PyTorch functions.
Trends of ICASSP 2022
The document summarizes key topics from ICASSP 2022, including general trends in speech and audio processing, self-supervised and contrastive learning approaches, security applications, and topics related to tasks like multilingualism and keyword spotting. Some of the main models and techniques discussed are Wav2vec, HuBERT, contrastive learning using Conformers, intermediate layer supervision in self-supervised learning, and anonymization of speech data for privacy.
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Merghani-SACNAS Poster
- 1. 1. Overview 3. Methodology 4. Results 7. Further Work Lexical Modeling of Egyptian Arabic for Automatic Speech Recognition Taha Merghani1 , Tuka Al Hanai2 and James Glass2 1 Department of Electrical & Computer Engineering, Jackson State University 2 MIT Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology Developing an Automatic Speech Recognition system (ASR) for Arabic is challenging. ● Ambiguous orthography ○ No Diacritics --information is missing. ○ Ambiguous word to phoneme mapping. ● Rich Morphology ○ Large number of combinations of stems+affixes. ● Diverse Dialects ○ Arabic widely spoken, 250+ million speakers. ○ No standardized orthography. ○ Limited data. ● Speech-enabled applications growing in everyday life (Apple Siri, Car Navigation, Medical Transcription). ● 6000+ languages in the world. Core of these systems is a recognizer: 2. MSRP Project ● Lexical Modelling ○ Diacritized lexicon with the aid of MADAMIRA, and Kaldi. ● Setup ○ Corpus: Al-Jazeera Egyptian Dialect. ○ 81K Tokens, 18K Vocab. ○ Training set: 10 hrs, 1hr development set, and 1hr evaluation set. ﻛﺗﺎﺑﮫ ﻓﻲ اﻟﻛﺎﺗب وﻛﺗب wktb AlkAtb fy ktAbh ndwrt thwrtr n hswrt ِﮫِِِﺑِﺗﺎﻛ ِﻲﻓ ُِبﺗَﺎﻛاﻟ ََبﺗَﻛو waktaba AlkAtibu fiy kitAbihi andwrote thewriter in hiswrit MADAMIRA ● Language Modelling ○ Trigram with SRILM Kneser-Ney discounting. ○ Built over training data transcript. References ● Pasha et al, “MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic”, 2014 ● F. Biadsy, N. Habash, and J. Hirschberg, “Improving the Arabic pronunciation dictionary for phone and word recognition with linguistically-based pronunciation rules,”, 2009 Research Question: Does diacritization of words improve ASR performance of Egyptian Arabic? Grapheme Lexicon Diacritized Lexicon ؟ أﻧت ﻣﯾن Language model Lexicon Acoustic Model Evaluate (WER) ؟ أﻧت ﻣﯾن Decoder ● Train new acoustic models ○ Long Short-Term Memory Recursive Neural Networks (LSTM-RNN) using Stacked Bottleneck features (SBN). ● Morpheme Based Language Modeling ○ Reduces Out-of-Vocabulary (OOV) rate of Dev/Eval sets. ● Explore larger lexicon sizes using tweets 6. Conclusion ● A graphemic lexicon (CODA) outperformed the diacritized lexicon. ● Reducing OOV improves WER. ● Using DNN and SDNN acoustic models improves WER. ● Acoustic Modeling ○ Features: MFCCs + CMVN + LDA + MLLT + SAT. ○ Triphone context-dependant GMM-HMM. ○ Deep Neural Networks (DNN). ○ Sequence Deep Neural Networks (SDNN). 5. Evaluation Comparison between reference (REF) and hypothesis (HYP) generated by ASR. Word Error Rate (WER) = (Subs + Dels + Ins) / N ● Subs: Number of substitutions between REF and HYP. ● Dels: Number of deletions in HYP compared to REF. ● Ins: Number of insertions in HYP compared to REF. ● N: Total number of words in REF. wktb : w k t b AlkAtb : A l K A t b fy : f y ktAbh : k t A b h wktb : w a k a t a b a AlkAtb : A l K A t i b u fy : f i y ktAbh : k i t A b i h i ○ Toolkits: Kaldi Speech Recognition, MADAMIRA text-processing. ○ Original transcription parsed and normalized to generate CODA transcription. Acoustic Model Diacritized Lexicon Grapheme Lexicon (CODA) Grapheme Lexicon (Original) Dev (%) Eval (%) Dev (%) Eval (%) Dev (%) Eval (%) GMM-HMM 56.8 59.1 57.4 58.6 60.0 61.5 DNN 1024 x 4 54.8 57.4 55.0 58.0 58.8 61.2 SDNN 1024 x 4 53.1 56.1 53.0 55.6 57.0 59.8 Lexicon Size 17.8K 17.5K 18.7K OOV (%) 15.3 15.3 16.6 #Lexical Units 43 36 35