10 HOURS DATA IS ALL YOU NEED Zeping MinQian GeZhong Liy Peking University

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10 HOURS DATA IS ALL YOU NEED

Zeping Min?Qian Ge?Zhong Li†

?Peking University

†Microsoft Research Asia

ABSTRACT

We propose a novel procedure to generate pseudo mandarin

speech data named as CAMP (character audio mix up), which

aims at generating audio from a character scale. We also

raise a method for building a mandarin character scale au-

dio database adaptive to CAMP named as META-AUDIO,

which makes full use of audio data and can greatly increase

the data diversity of the database. Experiments show that our

CAMP method is simple and quite effective. For example,

we train models with 10 hours of audio data in AISHELL-

1 and pseudo audio data generated by CAMP, and achieve

a competitive 11.07 character error rate (CER). Besides, we

also perform training with only 10 hours of audio data in

AIDATATANG dataset and pseudo audio data generated by

CAMP, which again achieves a competitive 8.26 CER.

Index Terms—Automatic speech recognition, data aug-

mentation, mandarin, pseudo label.

1. INTRODUCTION

Training a practical neural network (NN) model often requires

a large amount of labeled data. However, obtaining anno-

tated data in application is usually rather expensive and labor-

intensive. There are lots of efforts to reduce the dependence

of NN models on huge amounts of data, such as [1], and [2].

In the ﬁeld of speech recognition, it is equally necessary to

provide sufﬁcient training data for deep NN models. Inspired

by the pseudo label method ([2]) and mix up method ([1]), we

propose a novel procedure to generate pseudo-labeled data,

named as character audio mix up (CAMP), to alleviate the

heavy data dependence in automatic speech recognition.

In summary, our contributions are as follows:

• We successfully combine the advantages of pseudo la-

bel semi-supervised learning and mix up data augmen-

tation to propose a novel, simple and effective proce-

dure to generate pseudo-labeled speech data named as

character audio mix up (CAMP).

• We propose a META-AUDIO method for building a

mandarin character scale audio database adaptive to the

?Equal contribution

†Corresponding author

CAMP. The META-AUDIO takes full advantage of au-

dio data and can greatly increase the data diversity in

the database, as well as reduce the difﬁculty of building

the database.

• Experiments show that the CAMP and META-AUDIO

method are simple but quite effective. Training mod-

els with 10 hours of audio data in AISHELL-1 to-

gether with pseudo audio data generated by CAMP, we

achieve a competitive 11.07 character error rate (CER).

Besides, we also perform training with only 10 hours

of audio data in the AIDATATANG dataset and pseudo

audio data generated by CAMP, and again achieves a

competitive 8.26 CER.

2. RELATED WORK

A lot of effort has been made to obtain satisfying modeling

given a limited size of training samples. In application, one

of the most effective ways is data augmentation [3], and [4] ,

which is often designed carefully based on the nature of data

itself, and hence implies a certain pertinence. For instance,

in the ﬁeld of computer vision (CV), common approaches of

data augmentation ([5]) typically include cropping, rotation,

mix up ([1], [6]) and so on, which are speciﬁcally developed

for images. In the ﬁeld of automatic speech recognition, the

data augmentation is often conducted as follows. One way

is to perform data augmentation from the frequency domain.

For example, [7] implemented data augmentation by a ran-

dom linear transformation on the frequency dimension of the

spectrogram. Another way to perform data augmentation is

from the time domain. For example, in [8], a large amount

of audio in a noisy environment was synthesized by mixing

the clean audio with the noises, followed by an appropriate

ﬁltering through the average power.

Besides data augmentation, semi-supervised learning, to

improve the model performance with unlabeled training data,

is also popularly applicable and successful in many scenarios.

There are mainly two types of solutions for semi-supervised

learning. The ﬁrst is to take advantage of the continuity as-

sumption that if an actual perturbation is applied to an un-

labeled data, the prediction should not change signiﬁcantly.

Hence, minimizing the distance between the unlabeled data

arXiv:2210.13067v1 [cs.SD] 24 Oct 2022

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10HOURSDATAISALLYOUNEEDZepingMin?QianGe?ZhongLiy?PekingUniversityyMicrosoftResearchAsiaABSTRACTWeproposeanovelproceduretogeneratepseudomandarinspeechdatanamedasCAMP(characteraudiomixup),whichaimsatgeneratingaudiofromacharacterscale.Wealsoraiseamethodforbuildingamandarincharacterscaleau-diodatabasead...

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