Using sub-word n-gram models for dealing with OOV in large vocabulary speech recognition for Latvian
Code (0)
등록된 구현이 없습니다.
Tasks
Language Modellingspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Factored Neural Machine Translation
We present a new approach for neural machine translation (NMT) using the morphological and grammatical decomposition of the words (factors) in the output side of the neural network. This architecture addresses two main p…
Machine TranslationNMTTAGTranslationCNN-based Spoken Term Detection and Localization without Dynamic Programming
In this paper, we propose a spoken term detection algorithm for simultaneous prediction and localization of in-vocabulary and out-of-vocabulary terms within an audio segment. The proposed algorithm infers whether a term …
Word EmbeddingsAutomatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies
Today, the vocabulary size for language models in large vocabulary speech recognition is typically several hundreds of thousands of words. While this is already sufficient in some applications, the out-of-vocabulary word…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech RecognitionAutomatic Difficulty Assessment for Chinese Texts
We present a web-based interface that automatically assesses reading difficulty of Chinese texts. The system performs word segmentation, part-of-speech tagging and dependency parsing on the input text, and then determine…
Dependency ParsingLanguage AcquisitionPart-Of-Speech TaggingNeural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We model the output vocabulary of about 10…
Language ModelingLanguage Modellingspeech-recognitionSpeech Recognition