Spoken Language Recognition: From Fundamentals to Practice

By Haizhou Li, Bin Ma, Kong Aik LEE. 2013

324 citations recorded by the source corpus.

Spoken language recognition refers to the automatic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonological and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Li Deng
2. Dong Yu
3. Xiaodong He
4. Yoshua Bengio
5. Jianfeng Gao
6. Jinyu Li
7. Haizhou Li
8. Georg Heigold

In Signal Processing

1. Li Deng
2. Xiaodong He
3. Dong Yu
4. Haizhou Li
5. Jinyu Li
6. Hisashi Kawai
7. Georg Heigold
8. Weicheng Cai

Citations in the corpus

• Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System (2018, background): “…energies (Fbank), mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), shifted delta coeffi-cients (SDC) features [2, 5], and automatically learned phoneme discriminant features from deep neural networks (DNN), such as bottleneck features [6, 7, 8], phoneme posterior…”
• Feature Representation of Short Utterances Based on Knowledge Distillation for Spoken Language Identification (2018, background): “Spoken language identification (LID) is a task to determine which language is being spoken within a speech utterance [1, 2].”
• KALAKA-3: a database for the assessment of spoken language recognition technology on YouTube audios (2015, background): “A complete survey of state-of-the-art technologies, the most common benchmarks and metrics can be found in (Li et al. 2013).”
• Speech Recognition Using Deep Neural Networks: A Systematic Review (2019, background): “[13] presented the basics of the state of the art solutions for automatic spoken language recognition for both, computational and phonological perspectives.”
• Interactive Learning of Teacher-student Model for Short Utterance Spoken Language Identification (2019, background): “Spoken language identification (LID) techniques are important for multilingual applications, such as multilingual automatic speech recognition and translation systems [1, 2].”
• Optimization Algorithms and Applications for Speech and Language Processing (2013, background): “Since that time, important progress has been made in a wide span of MT component technologies, including word alignment, phrase-based MT methods [33], hierarchical phrasebased methods, syntax-based methods, discriminative training methods [34], [35], model adaptationmethods, and system combination…”
• Frame-by-frame language identification in short utterances using deep neural networks (2015, background): “Driven by recent developments in speaker verification, the current state-of-the-art in acoustic LID systems involves using i-vector front-end features followed by diverse classification mechanisms that compensate speaker and session variabilities [7] [10] [11].”
• Knowledge Distillation-Based Representation Learning for Short-Utterance Spoken Language Identification (2020, methodology): “We set [a, b] as [1, 10] for the student model and [5, 10] for the teacher model.”