A.I. based Embedded Speech to Text Using Deepspeech
Deepspeech was very useful for development IoT devices that need voice recognition. One of the voice recognition systems is deepspeech from Mozilla. Deepspeech is an open-source voice recognition that was using a neural network to convert speech spectrogram into a text transcript. This paper shows t...
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| creator | Firmansyah, Muhammad Hafidh Paul, Anand Bhattacharya, Deblina Urfa, Gul Malik |
| description | Deepspeech was very useful for development IoT devices that need voice
recognition. One of the voice recognition systems is deepspeech from Mozilla.
Deepspeech is an open-source voice recognition that was using a neural network
to convert speech spectrogram into a text transcript. This paper shows the
implementation process of speech recognition on a low-end computational device.
Development of English-language speech recognition that has many datasets
become a good point for starting. The model that used results from pre-trained
model that provide by each version of deepspeech, without change of the model
that already released, furthermore the benefit of using raspberry pi as a media
end-to-end speech recognition device become a good thing, user can change and
modify of the speech recognition, and also deepspeech can be standalone device
without need continuously internet connection to process speech recognition,
and even this paper show the power of Tensorflow Lite can make a significant
difference on inference by deepspeech rather than using Tensorflow
non-Lite.This paper shows the experiment using Deepspeech version 0.1.0, 0.1.1,
and 0.6.0, and there is some improvement on Deepspeech version 0.6.0, faster
while processing speech-to-text on old hardware raspberry pi 3 b+. |
| doi_str_mv | 10.48550/arxiv.2002.12830 |
| format | Article |
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recognition. One of the voice recognition systems is deepspeech from Mozilla.
Deepspeech is an open-source voice recognition that was using a neural network
to convert speech spectrogram into a text transcript. This paper shows the
implementation process of speech recognition on a low-end computational device.
Development of English-language speech recognition that has many datasets
become a good point for starting. The model that used results from pre-trained
model that provide by each version of deepspeech, without change of the model
that already released, furthermore the benefit of using raspberry pi as a media
end-to-end speech recognition device become a good thing, user can change and
modify of the speech recognition, and also deepspeech can be standalone device
without need continuously internet connection to process speech recognition,
and even this paper show the power of Tensorflow Lite can make a significant
difference on inference by deepspeech rather than using Tensorflow
non-Lite.This paper shows the experiment using Deepspeech version 0.1.0, 0.1.1,
and 0.6.0, and there is some improvement on Deepspeech version 0.6.0, faster
while processing speech-to-text on old hardware raspberry pi 3 b+.</description><identifier>DOI: 10.48550/arxiv.2002.12830</identifier><language>eng</language><subject>Computer Science - Learning ; Computer Science - Sound</subject><creationdate>2020-02</creationdate><rights>http://creativecommons.org/licenses/by-nc-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,778,883</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2002.12830$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2002.12830$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Firmansyah, Muhammad Hafidh</creatorcontrib><creatorcontrib>Paul, Anand</creatorcontrib><creatorcontrib>Bhattacharya, Deblina</creatorcontrib><creatorcontrib>Urfa, Gul Malik</creatorcontrib><title>A.I. based Embedded Speech to Text Using Deepspeech</title><description>Deepspeech was very useful for development IoT devices that need voice
recognition. One of the voice recognition systems is deepspeech from Mozilla.
Deepspeech is an open-source voice recognition that was using a neural network
to convert speech spectrogram into a text transcript. This paper shows the
implementation process of speech recognition on a low-end computational device.
Development of English-language speech recognition that has many datasets
become a good point for starting. The model that used results from pre-trained
model that provide by each version of deepspeech, without change of the model
that already released, furthermore the benefit of using raspberry pi as a media
end-to-end speech recognition device become a good thing, user can change and
modify of the speech recognition, and also deepspeech can be standalone device
without need continuously internet connection to process speech recognition,
and even this paper show the power of Tensorflow Lite can make a significant
difference on inference by deepspeech rather than using Tensorflow
non-Lite.This paper shows the experiment using Deepspeech version 0.1.0, 0.1.1,
and 0.6.0, and there is some improvement on Deepspeech version 0.6.0, faster
while processing speech-to-text on old hardware raspberry pi 3 b+.</description><subject>Computer Science - Learning</subject><subject>Computer Science - Sound</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzs1qwzAQBGBdeghuHiCn6gWsrrSyZR9D_iGQQ9yzWVmr1tA0xg4lefskbk8zMDB8Qsw0KFtkGbxTf21_lQEwSpsCYSJwrnZKeho4yNXJcwiPcuyYmy95OcuKrxf5MbQ_n3LJ3A3j8CpeIn0PPP3PRFTrVbXYpvvDZreY71PKHaRlwwX7MuiYm4AuOgQypS81xZjpxuQu55g5r10Ihr31sfAWAck2YJEAE_H2dzuq665vT9Tf6qe-HvV4ByV1PdI</recordid><startdate>20200225</startdate><enddate>20200225</enddate><creator>Firmansyah, Muhammad Hafidh</creator><creator>Paul, Anand</creator><creator>Bhattacharya, Deblina</creator><creator>Urfa, Gul Malik</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200225</creationdate><title>A.I. based Embedded Speech to Text Using Deepspeech</title><author>Firmansyah, Muhammad Hafidh ; Paul, Anand ; Bhattacharya, Deblina ; Urfa, Gul Malik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-9ce8eb9d1f62d37f730a29b91aff51c2676ef57b17dd2eb4bf8b4303a4c043a03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Learning</topic><topic>Computer Science - Sound</topic><toplevel>online_resources</toplevel><creatorcontrib>Firmansyah, Muhammad Hafidh</creatorcontrib><creatorcontrib>Paul, Anand</creatorcontrib><creatorcontrib>Bhattacharya, Deblina</creatorcontrib><creatorcontrib>Urfa, Gul Malik</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Firmansyah, Muhammad Hafidh</au><au>Paul, Anand</au><au>Bhattacharya, Deblina</au><au>Urfa, Gul Malik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A.I. based Embedded Speech to Text Using Deepspeech</atitle><date>2020-02-25</date><risdate>2020</risdate><abstract>Deepspeech was very useful for development IoT devices that need voice
recognition. One of the voice recognition systems is deepspeech from Mozilla.
Deepspeech is an open-source voice recognition that was using a neural network
to convert speech spectrogram into a text transcript. This paper shows the
implementation process of speech recognition on a low-end computational device.
Development of English-language speech recognition that has many datasets
become a good point for starting. The model that used results from pre-trained
model that provide by each version of deepspeech, without change of the model
that already released, furthermore the benefit of using raspberry pi as a media
end-to-end speech recognition device become a good thing, user can change and
modify of the speech recognition, and also deepspeech can be standalone device
without need continuously internet connection to process speech recognition,
and even this paper show the power of Tensorflow Lite can make a significant
difference on inference by deepspeech rather than using Tensorflow
non-Lite.This paper shows the experiment using Deepspeech version 0.1.0, 0.1.1,
and 0.6.0, and there is some improvement on Deepspeech version 0.6.0, faster
while processing speech-to-text on old hardware raspberry pi 3 b+.</abstract><doi>10.48550/arxiv.2002.12830</doi><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_arxiv_primary_2002_12830 |
| source | arXiv.org |
| subjects | Computer Science - Learning Computer Science - Sound |
| title | A.I. based Embedded Speech to Text Using Deepspeech |
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