Spoken language Identification (LID) systems are needed to identify the
language(s) present in a given audio sample, and typically could be the first
step in many speech processing related tasks such as automatic speech
recognition (ASR). Automatic identification of the languages present in a
speech signal is not only scientifically interesting, but also of practical
importance in a multilingual country such as India. In many of the Indian
cities, when people interact with each other, as many as three languages may
get mixed. These may include the official language of that province, Hindi and
English (at times the languages of the neighboring provinces may also get mixed
during these interactions). This makes the spoken LID task extremely
challenging in Indian context. While quite a few LID systems in the context of
Indian languages have been implemented, most such systems have used small scale
speech data collected internally within an organization. In the current work,
we perform spoken LID on three Indian languages (Gujarati, Telugu, and Tamil)
code-mixed with English. This task was organized by the Microsoft research team
as a spoken LID challenge. In our work, we modify the usual spectral
augmentation approach and propose a language mask that discriminates the
language ID pairs, which leads to a noise robust spoken LID system. The
proposed method gives a relative improvement of approximately 3-5% in the LID
accuracy over a baseline system proposed by Microsoft on the three language
pairs for two shared tasks suggested in the challenge.

本研究针对印度语境下多种语言混杂的情况，对在语音处理中较为基础的语种识别系统 LID 进行优化，提出基于语种掩蔽和光谱增强的方法，在微软研究团队提出的挑战任务中相对基线系统进行了 3-5% 的 LID 准确度改进。

利用频谱增强技术进行混合语言口语识别

Exploiting Spectral Augmentation for Code-Switched Spoken Language  Identification

Language Identification (LID) systems are used to classify the spoken
language from a given audio sample and are typically the first step for many
spoken language processing tasks, such as Automatic Speech Recognition (ASR)
systems. Without automatic language detection, speech utterances cannot be
parsed correctly and grammar rules cannot be applied, causing subsequent speech
recognition steps to fail. We propose a LID system that solves the problem in
the image domain, rather than the audio domain. We use a hybrid Convolutional
Recurrent Neural Network (CRNN) that operates on spectrogram images of the
provided audio snippets. In extensive experiments we show, that our model is
applicable to a range of noisy scenarios and can easily be extended to
previously unknown languages, while maintaining its classification accuracy. We
release our code and a large scale training set for LID systems to the
community.

该论文提出了基于图像领域解决 LID 问题的模型，使用了混合的卷积循环神经网络技术对提供的音频片段的频谱图像进行分析，并在广泛的实验中证明了模型的应用性，能够轻松地扩展到以前未知的语言，同时保持其分类准确性。