Automatic spoken language identification utilizing acoustic and phonetic speech information
Explore this paper's citation graph
Summary
The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology Language Recognition Evaluation.
- Type
- dissertation
- Published
- 2004-01-01
- Cited by
- 23
- References
- 85
- OpenAlex
- https://openalex.org/W207426585
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5864180
Keywords
Computer science, Utterance, Speech recognition, Spoken language, NIST
References
- A study of computation speed-UPS of the GMM-UBM speaker recognition system
- The OGI 22 language telephone speech corpus
- Methods to improve Gaussian mixture model based language identification system
- Predicting, diagnosing and improving automatic language identification performance
- Fast bootstrapping of LVCSR systems with multilingual phoneme sets
- Two novel language model estimation techniques for statistical language identification
- Comparison of background normalization methods for text-independent speaker verification
- Language identification incorporating lexical information
- Spoken language discrimination using speech fundamental frequency
- Speaker-independent, text-independent language identification by HMM
- Automatic segmentation and identification of ten languages using telephone speech
- A comparison of approaches to automatic language identification using telephone speech
- Automatic language identification with perceptually guided training and recurrent neural networks
- Cross-lingual pronunciation modelling for indonesian speech recognition
- The OGI multi-language telephone speech corpus
- Optimizing baseforms for HMM-based speech recognition
- Language identification with embedded word models
- Automatic Language Discrimination.
- Language independent and language adaptive large vocabulary speech recognition
- Experiments in language identification with neural networks
Cited by
- Towards improved speech recognition for resource poor languages
- IITKGP-MLILSC speech database for language identification
- Language Identification using Warping and the Shifted Delta Cepstrum
- Experiments on automatic language identification for philippine languages using acoustic Gaussian Mixture Models
- Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features
- Language Identification: A Tutorial
- Pair-wise language discrimination using phonotactic information
- A Comparative evaluation of three automatic language identification approaches for Indian Languages
- Feature Extraction Methods in Language Identification: A Survey
- Exploration of sparse representation techniques in language recognition
- A comparison between phonetic engine and GMM–UBM classifier for language identification tasks
- Research on the Development of Automatic Dialect Identification Technology Based on Language Recognition
- Language ID Prediction from Speech Using Self-Attentive Pooling
- Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions
- A review into deep learning techniques for spoken language identification
- Development of novel automated language classification model using pyramid pattern technique with speech signals
- A Study of Language Identification and ASR for Telugu and English
- Language-dependent Fusion for Language Identification
- Development, implementation and testing of language identification system for seven Philippine languages
- Spoken Language Identification Using Ergodic Hidden Markov Models
Related papers
- A unified framework to incorporate speech and language information in spoken language processing
- INTEGRATING LANGUAGE IDENTIFICATION TO IMPROVE MULTILINGUAL SPEECH RECOGNITION
- Phonological and computational perspectives of language identification (LID) system
- Multi-lingual speech recognition system for speech-to-speech translation
- Two Approaches to Class-Based Language Models for ASR
- First steps in fast acoustic modeling for a new target language: application to Vietnamese
- Exploring an unsupervised, language independent, spoken document retrieval system
- Segment-based automatic language identification