To overpass the disparity between theory and applications in language-related technology in the text as well as speech and several other areas, a well-designed and well-developed corpus is essential. Several problems and issues encountered while developing a corpus, especially for low resource languages. The Malayalam Speech Corpus (MSC) is one of the first open speech corpora for Automatic Speech Recognition (ASR) research to the best of our knowledge. It consists of 250 hours of Agricultural speech data. We are providing a transcription file, lexicon, and annotated speech along with the audio segment. It is available in future for public use upon request at "www.iiitmk.ac.in/vrclc/utilities/ml speechcorpus". This paper details the development and collection process in the domain of agricultural speech corpora in the Malayalam Language.

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Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation, pages 25–28

Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020

c

European Language Resources Association (ELRA), licensed under CC-BY-NC

25

Malayalam Speech Corpus: Design and Development for Dravidian Language

Lekshmi.K.R, Jithesh V S, Elizabeth Sherly

Research Scholar, Senior Linguist, Senior Professor

Bharathiar University, IIITM-K, IIITM-K

lekshmi.kr@iiitmk.ac.in, jithesh.vs@iiitmk.ac.in, sherly@iiitmk.ac.in

Abstract

To overpass the disparity between theory and applications in language-related technology in the text as well as speech and several other

areas, a well-designed and well-developed corpus is essential. Several problems and issues encountered while developing a corpus,

especially for low resource languages. The Malayalam Speech Corpus (MSC) is one of the first open speech corpora for Automatic

Speech Recognition (ASR) research to the best of our knowledge. It consists of 250 hours of Agricultural speech data. We are providing

a transcription file, lexicon and annotated speech along with the audio segment. It is available in future for public use upon request at

"www.iiitmk.ac.in/vrclc/utilities/ml speechcorpus". This paper details the development and collection process in the domain of agricul-

tural speech corpora in the Malayalam Language.

Keywords: Malayalam, ASR, Agricultural Speech corpus, Narrational and Interview Speech Corpora

1. Introduction

Malayalam is the official language of Kerala, Lakshad-

weep, and Mahe. From 1330 million people in India,

37 million people speak Malayalam ie; 2.88% of Indians.

(Wikipedia contributors, 2020). Malayalam is the youngest

of all languages in the Dravidian family. Four or five

decades were taken for Malayalam to emerge from Tamil.

The development of Malayalam is greatly influenced by

Sanskrit also.

In the Automatic Speech Recognition (ASR) area many

works are progressing in highly and low-resourced lan-

guages. The present speech recognition system has

achieved a 'Natural' degree of accuracy mainly in Standard

American English (Xiong et al., 2016). The accurate recog-

nition of speech exists only for highly resourced languages.

But it is still lagging for "non-native" speakers. To increase

the accuracy of such an ASR system the speech data for

low- resource language like Malayalam is to be increased.

To encourage the research on speech technology and its

related applications in Malayalam, a collection of speech

corpus is commissioned and named as Malayalam Speech

Corpus (MSC). The corpus consists of the following parts.

•200 hours of Narrational Speech named NS and

•50 hours of Interview Speech named IS

The raw speech data is collected from "Kissan Kr-

ishideepam" an agriculture-based program in Malayalam

by the Department of Agriculture, Government of Kerala.

The NS is created by making a script during the post-

production stage and dubbed with the help of people in dif-

ferent age groups and gender but they are amateur dubbing

artists. The speech data is thoughtfully designed - for var-

ious applications like code mixed language analysis, Auto-

matic Speech Recognition (ASR) related research, speaker

recognition – by considering sociolinguistic variables.

This paper represents the development of Narrational and

Interview Speech corpora (NS and IS) collected from na-

tive Malayalam speakers. The literature survey of different

speech corpora creation is detailed in section 2. Section 3

describes the design and demographics of speech data. The

section 4 continues with transcription and section 5 deals

with lexicon of the speech data and paper concludes with

section 6.

2. Literature Survey

Many languages have developed speech corpus and they

are open source too. The English read speech corpus is

freely available to download for research purposes (Koh et

al., 2019) (Panayotov et al., 2015). Similarly, a database

is made available with the collection of TED talks in the

English language (Hernandez et al., 2018). Databases are

available for Indian languages on free download and a pay-

ment basis also. For the Malayalam language-based emo-

tion recognition, a database is available (Rajan et al., 2019).

The corpus collection of low resourced languages is a good

initiative in the area of ASR. One of such work is done on

Latvian language (Pinnis et al., 2014). They created 100

hours of orthographically transcribed audio data and anno-

tated corpus also. In addition to that a four hours of phonet-

ically transcribed audio data is also available. The authors

presented the statistics of speech corpus along with criteria

for design of speech corpus.

South Africa has eleven official languages. An attempt is

made for the creation of speech corpora on these under re-

sourced languages (Barnard et al., 2014). A collection of

more than 50 hours of speech in each language is made

available. They validated the corpora by building acoustic

and language model using KALDI.

Similarly speech corpora for North-East Indian low-

resourced languages is also created (Hernandez et al.,

2018). The authors collected speech and text corpora on

Assamese, Bengali and Nepali. They conducted a statisti-

26

cal study of the corpora also.

3. The Speech Corpora

A recording studio is setup at our visual media lab with

a quiet and sound proof room. A standing microphone

is used for recording NS corpora. IS corpora is collected

directly from the farmers using recording portable Mic at

their place. Hundred speakers are involved in the recording

of NS and IS corpora.

3.1. Narrational and Interview Speech Corpora

The written agricultural script, which is phonetically bal-

anced and phonetically rich (up to triphone model), was

given to the speakers to record the Narrational Speech.

Scripts were different in content. An example script is pro-

vided in Fig:1. They were given enough time to record the

data. If any recording issues happened, after rectification

by the recording assistant it was rerecorded.

Figure 1: Example of script file for dubbing

The Narrational Speech is less expensive than Interview

Speech because it is difficult to get data for the ASR system.

The IS data is collected in a face-to-face interview style.

The literacy and the way to communicate information flu-

ently have given less focus. The interviewee with enough

experience in his field of cultivation is asked to speak about

his cultivation and its features. The interviewer should be

preferably a subject expert in the area of cultivation. Both

of them are given separate microphones for this purpose.

Few challenges were faced during the recording of the

speech corpus. There were lot of background noise like

sounds of vehicles, animals, birds, irrigation motor and

wind. Another main issue that happened during post pro-

duction is the difference in pronunciation styles in the In-

terview Speech corpora collection. This caused difficulty

during validation of the corpus. The recording used to ex-

tend up to 5-6 hours depending on speakers. The recorded

data is then given for post-production to clean unwanted

information from that.

3.2. Speaker Criteria

We have set a few criteria for recording the Narrational

Speech data.

•The speakers are at minimum age of 18

•They are citizens of India

•Speakers are residents of Kerala

•The mother tongue of the speaker should be Malay-

alam without any specific accents

3.3. Recording Specifications

Speech data is collected with two different microphones for

NS and IS. For Narrational Speech, Shure SM58-LC car-

dioid vocal microphone without cable is used. For IS, we

utilized Sennheiser XSW 1-ME2-wireless presentation mi-

crophone of range 548-572 MHz Steinberg Nuendo and Pro

Tools are used for the audio post-production process.

The audio is recorded in 48 kHz sampling frequency and

16 bit sampling rate for broadcasting and the same is down

sampled to 16 kHz sampling frequency and 16 bit sampling

rate for speech-related research purposes. The recordings

of speech corpora are saved in WAV files.

3.4. Demographics

MSC aims to present a good quality audio recording for

speech related research. The NS and IS corpus have both

male and female speakers. In NS, the male and female

speakers are made up with 75% and 25% respectively. IS

have more male speakers than females with 82% and 18%

of total speakers.The other demographics available from

the collected data are Community, Place of Cultivation and

Type of Cultivation.

Category NS (%) IS (%)

Hindu 85 51

Christian 10 35

Muslim 05 14

Total 100 100

Table 1: Demographic details of speakers by community

Table 2 and 3 contains the details of the place of cultivation

and the type of cultivation in Kerala.

27

Place of Cultivation (District wise) IS(%)

Thiruvananthapuram 26

Kollam 21

Pathanamthitta 02

Ernakulam 07

Alappuzha 08

Kottayam 08

Idukki 09

Thrissur 12

Wayanad 03

Kozhikode 02

Kannur 02

Total 100

Table 2: Demographic details of speakers by place of culti-

vation

Type of Cultivation IS (%)

Animal Husbandry 10

Apiculture 11

Diary 16

Fish and crab farming 05

Floriculture 07

Fruits and vegetables 22

Horticulture 04

Mixed farming 07

Organic farming 08

Poultry 07

Terrace farming 03

Total 100

Table 3: Demographic details of speakers by type of culti-

vation

4. Transcription

The NS and IS corpora are transcribed orthographically into

Malayalam text. The transcribers are provided with the au-

dio segments that the speaker read. Their task is to tran-

scribe the content of the audio into Malayalam and into

phonetic text. A sample of three transcribed data with de-

mographic details is shown below and the annotated speech

of first two sentences is depicted in Fig 2.

Figure 2: An example of Annotated Speech Corpora

Sample 1:Record Entry No : 180220 01 01

In the first sample a Narrational Speech is detailed. The

narrator is about 45 years old and he is describing the de-

tails about Palakkad a district in Kerala and a mango estate

there. Few sentences are displayed below.

Sentence 1:

<Without saying we can understand that it is Palakkad>

Sentence 2:

<Kerala's Castle door>

Sentence 3:

<Selam dharmapuri and krishnagiri are the birthplaces of

Malgova>

Sample 2: Record Entry 2: 180220 02 01

The sample shown below is an Interview Speech. The in-

terviewer is an agriculture officer of age 50 and interviewee

is the owner of farm about 55 years old.

Sentence 1:

<Do you think you could fulfill what you have wished

or envisioned from the desert, here in your homeland,

Kerala?>

Sentence 2:

28

<Definitely we could. What we have planted here by our-

selves blossomed, bore fruit, relished it and shared it with

our dear ones>

5. Lexicon

The pronunciation dictionary, called Lexicon contains a

collection of unique 4925 words. The audio collection pro-

cess is still going on which will increase the lexicon size.

The lexicon consists of word and its corresponding phone-

mic and syllabic representation as in the example shown in

Fig 3.

Figure 3: Example of the lexicon

6. Conclusion

Speech is the primary and natural mode of communication

than writing. It is possible to extract more linguistic in-

formation from speech than text like emotions and accent.

Speech related applications are more useful for illiterate

and old people. The articulatory and acoustic information

can be obtained from a good audio recording environment.

One of the important features of speech data is that, there

is less interference from a second party compared to textual

data.

To encourage the academic research in speech related appli-

cations, a good number of multilingual and multipurpose

speech corpora for Indian languages is required. The re-

sponsibility to develop such corpora still lies on the shoul-

der of the concerned researcher. Also the role of language

corpora is very significant to preserve and maintain the lin-

guistic heritage of our country.

The release of MSC will be one of the first speech cor-

pora of Malayalam, contributing 200 hours of Narrational

Speech and 50 hours of Interview Speech data for public

use. The lexicon and annotated speech is also made avail-

able with the data. Future work includes creation of corpora

related to tourism and entertainment domains and enhance-

ment of quality of speech by building an ASR using KALDI

toolkit. The updates on corpus will be accessible through

"www.iiitmk.ac.in/vrclc/utilities/ml speechcorpus".

Acknowledgements

This research is supported by the Kerala State Council for

Science, Technology and Environment (KSCSTE). I thank

KSCSTE for funding the project under the Back-to-lab

scheme. I also thank Agri team, Indian Institute of Informa-

tion Technology and Management-Kerala, Kissan Project

for collecting the audio data.

Bibliographical References

Barnard, E., Davel, M. H., van Heerden, C., De Wet, F., and

Badenhorst, J. (2014). The nchlt speech corpus of the

south african languages. In Workshop Spoken Language

Technologies for Under-resourced Languages (SLTU).

Hernandez, F., Nguyen, V., Ghannay, S., Tomashenko, N.,

and Est`

eve, Y. (2018). Ted-lium 3: twice as much data

and corpus repartition for experiments on speaker adap-

tation. In International Conference on Speech and Com-

puter, pages 198–208. Springer.

Koh, J. X., Mislan, A., Khoo, K., Ang, B., Ang, W., Ng, C.,

and Tan, Y.-Y. (2019). Building the singapore english

national speech corpus. Malay, 20(25.0):19–3.

Panayotov, V., Chen, G., Povey, D., and Khudanpur, S.

(2015). Librispeech: an asr corpus based on public do-

main audio books. In 2015 IEEE International Con-

ference on Acoustics, Speech and Signal Processing

(ICASSP), pages 5206–5210. IEEE.

Pinnis, M., Auzina, I., and Goba, K. (2014). Designing

the latvian speech recognition corpus. In LREC, pages

1547–1553.

Rajan, R., Haritha, U., Sujitha, A., and Rejisha, T. (2019).

Design and development of a multi-lingual speech cor-

pora (tamar-emodb) for emotion analysis. Proc. Inter-

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Wikipedia contributors. (2020). Malayalam — Wikipedia,

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2020].

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arXiv preprint arXiv:1610.05256.

ResearchGate has not been able to resolve any citations for this publication.

The NCHLT speech corpus contains wide-band speech from approximately 200 speakers per language, in each of the eleven official languages of South Africa. We describe the design and development processes that were undertaken in order to develop the corpus, and report on associated materials such as orthographic transcriptions and pronunciation dictionaries that were released as part of the corpus. In order to benchmark speechrecognition performance on the corpus, we have also developed both phone-recognition and word-recognition systems for all eleven languages; we find that high accuracies can be achieved for these speaker-independent but vocabulary-dependent recognition tasks in all languages.

In this paper the authors present the first Latvian speech corpus designed specifically for speech recognition purposes. The paper outlines the decisions made in the corpus designing process through analysis of related work on speech corpora creation for different languages. The authors provide also guidelines that were used for the creation of the Latvian speech recognition corpus. The corpus creation guidelines are fairly general for them to be re-used by other researchers when working on different language speech recognition corpora. The corpus consists of two parts – an orthographically annotated corpus containing 100 hours of orthographically transcribed audio data and a phonetically annotated corpus containing 4 hours of phonetically transcribed audio data. Metadata files in XML format provide additional details about the speakers, noise levels, speech styles, etc. The speech recognition corpus is phonetically balanced and phonetically rich and the paper describes also the methodology how the phonetical balancedness has been assessed.

Ted-lium 3: twice as much data and corpus repartition for experiments on speaker adaptation

  • F Hernandez
  • V Nguyen
  • S Ghannay
  • N Tomashenko
  • Y Estève

Hernandez, F., Nguyen, V., Ghannay, S., Tomashenko, N., and Estève, Y. (2018). Ted-lium 3: twice as much data and corpus repartition for experiments on speaker adaptation. In International Conference on Speech and Computer, pages 198-208. Springer.

  • W Xiong
  • J Droppo
  • X Huang
  • F Seide
  • M Seltzer
  • A Stolcke
  • D Yu
  • G Zweig

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., and Zweig, G. (2016). Achieving human parity in conversational speech recognition. arXiv preprint arXiv:1610.05256.