Papers Text Normalization
“Text Normalization” 태그가 달린 논문 145편 · 필터 해제
Normalization of Lithuanian Text Using Regular Expressions
Text Normalization is an integral part of any text-to-speech synthesis system. In a natural language text, there are elements such as numbers, dates, abbreviations, etc. that belong to other semiotic classes. They are ca…
Speech SynthesisText Normalizationtext-to-speechText to Speech+1Extending Whisper with prompt tuning to target-speaker ASR
Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech of a target speaker from multi-talker overlapped utterances. Most of the existing target-speaker ASR (TS-ASR) methods involve either…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)parameter-efficient fine-tuningspeech-recognition+2A Chat About Boring Problems: Studying GPT-based text normalization
Text normalization - the conversion of text from written to spoken form - is traditionally assumed to be an ill-formed task for language models. In this work, we argue otherwise. We empirically show the capacity of Large…
Prompt EngineeringText NormalizationImproving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method
Inverse text normalization (ITN) is crucial for converting spoken-form into written-form, especially in the context of automatic speech recognition (ASR). While most downstream tasks of ASR rely on written-form, ASR syst…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Data AugmentationForm+6LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues
Emotion recognition in conversations (ERC) is vital to the advancements of conversational AI and its applications. Therefore, the development of an automated ERC model using the concepts of machine learning (ML) would be…
BinarizationEmotion RecognitionText NormalizationA unified front-end framework for English text-to-speech synthesis
The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phon…
Speech SynthesisText Normalizationtext-to-speechText to Speech+1Language Agnostic Data-Driven Inverse Text Normalization
With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need. This inverse text normalization (ITN) problem attracts the attention of r…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Data AugmentationForm+3Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition. In this paper, we focus on the question of robust and fair evaluation metrics. To that end, we develop a refere…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Benchmarkingspeech-recognition+3Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition
Automatic Speech Recognition (ASR) systems typically yield output in lexical form. However, humans prefer a written form output. To bridge this gap, ASR systems usually employ Inverse Text Normalization (ITN). In previou…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition+2TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection
Punctuation and Segmentation are key to readability in Automatic Speech Recognition (ASR), often evaluated using F1 scores that require high-quality human transcripts and do not reflect readability well. Human evaluation…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Model SelectionSegmentation+3Four-in-One: A Joint Approach to Inverse Text Normalization, Punctuation, Capitalization, and Disfluency for Automatic Speech Recognition
Features such as punctuation, capitalization, and formatting of entities are important for readability, understanding, and natural language processing tasks. However, Automatic Speech Recognition (ASR) systems produce sp…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Formspeech-recognition+2Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization
Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole lan…
Stance DetectionText NormalizationTranslationNon-Standard Vietnamese Word Detection and Normalization for Text-to-Speech
Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenge…
ArticlesSentenceText Normalizationtext-to-speech+1Thutmose Tagger: Single-pass neural model for Inverse Text Normalization
Inverse text normalization (ITN) is an essential post-processing step in automatic speech recognition (ASR). It converts numbers, dates, abbreviations, and other semiotic classes from the spoken form generated by ASR to …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)HallucinationMachine Translation+4Improving Data Driven Inverse Text Normalization using Data Augmentation
Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. Traditional handcrafted ITN rules can be complex to transcribe and maintain. M…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Data AugmentationForm+3Text normalization for low-resource languages: the case of Ligurian
Text normalization is a crucial technology for low-resource languages which lack rigid spelling conventions or that have undergone multiple spelling reforms. Low-resource text normalization has so far relied upon hand-cr…
Text NormalizationAdversarial Text Normalization
Text-based adversarial attacks are becoming more commonplace and accessible to general internet users. As these attacks proliferate, the need to address the gap in model robustness becomes imminent. While retraining on a…
Adversarial TextNatural Language InferenceText NormalizationBoring Problems Are Sometimes the Most Interesting
In a recent position paper, Turing Award Winners Yoshua Bengio, Geoffrey Hinton, and Yann LeCun make the case that symbolic methods are not needed in AI and that, while there are still many issues to be resolved, AI will…
Text NormalizationJHU IWSLT 2022 Dialect Speech Translation System Description
This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task. Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT). We …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Machine Translationspeech-recognition+3An End-to-end Chinese Text Normalization Model based on Rule-guided Flat-Lattice Transformer
Text normalization, defined as a procedure transforming non standard words to spoken-form words, is crucial to the intelligibility of synthesized speech in text-to-speech system. Rule-based methods without considering co…
Text Normalizationtext-to-speechText to Speech