speech coding

{{Short description|Lossy audio compression applied to human speech}}

Speech coding is an application of data compression to digital audio signals containing speech. Speech coding uses speech-specific parameter estimation using audio signal processing techniques to model the speech signal, combined with generic data compression algorithms to represent the resulting modeled parameters in a compact bitstream.{{cite journal|first1=M. |last1=Arjona Ramírez|first2=M.|last2=Minam|title=Low bit rate speech coding|journal=Wiley Encyclopedia of Telecommunications, J. G. Proakis, Ed.|location=New York| publisher=Wiley|year=2003| volume= 3|pages=1299–1308}}

Common applications of speech coding are mobile telephony and voice over IP (VoIP).M. Arjona Ramírez and M. Minami, "Technology and standards for low-bit-rate vocoding methods," in The Handbook of Computer Networks, H. Bidgoli, Ed., New York: Wiley, 2011, vol. 2, pp. 447–467. The most widely used speech coding technique in mobile telephony is linear predictive coding (LPC), while the most widely used in VoIP applications are the LPC and modified discrete cosine transform (MDCT) techniques.{{Citation needed|date=December 2019}}

The techniques employed in speech coding are similar to those used in audio data compression and audio coding where appreciation of psychoacoustics is used to transmit only data that is relevant to the human auditory system. For example, in voiceband speech coding, only information in the frequency band 400 to 3500 Hz is transmitted but the reconstructed signal retains adequate intelligibility.

Speech coding differs from other forms of audio coding in that speech is a simpler signal than other audio signals, and statistical information is available about the properties of speech. As a result, some auditory information that is relevant in general audio coding can be unnecessary in the speech coding context. Speech coding stresses the preservation of intelligibility and pleasantness of speech while using a constrained amount of transmitted data.P. Kroon, "Evaluation of speech coders," in Speech Coding and Synthesis, W. Bastiaan Kleijn and K. K. Paliwal, Ed., Amsterdam: Elsevier Science, 1995, pp. 467-494. In addition, most speech applications require low coding delay, as latency interferes with speech interaction.J. H. Chen, R. V. Cox, Y.-C. Lin, N. S. Jayant, and M. J. Melchner, A low-delay CELP coder for the CCITT 16 kb/s speech coding standard. IEEE J. Select. Areas Commun. 10(5): 830-849, June 1992.

Sample companding viewed as a form of speech coding

The A-law and μ-law algorithms used in G.711 PCM digital telephony can be seen as an earlier precursor of speech encoding, requiring only 8 bits per sample but giving effectively 12 bits of resolution.{{cite book|first1=N. S. |last1=Jayant|first2=P.|last2= Noll|title= Digital coding of waveforms|location= Englewood Cliffs|publisher= Prentice-Hall|year=1984}} Logarithmic companding are consistent with human hearing perception in that a low-amplitude noise is heard along a low-amplitude speech signal but is masked by a high-amplitude one. Although this would generate unacceptable distortion in a music signal, the peaky nature of speech waveforms, combined with the simple frequency structure of speech as a periodic waveform having a single fundamental frequency with occasional added noise bursts, make these very simple instantaneous compression algorithms acceptable for speech.{{citation needed|date=July 2023}}{{dubious|discuss=Logarithmic companding for music|date=July 2023}}

A wide variety of other algorithms were tried at the time, mostly delta modulation variants, but after careful consideration, the A-law/μ-law algorithms were chosen by the designers of the early digital telephony systems. At the time of their design, their 33% bandwidth reduction for a very low complexity made an excellent engineering compromise. Their audio performance remains acceptable, and there was no need to replace them in the stationary phone network.{{citation needed|date=July 2023}}

In 2008, G.711.1 codec, which has a scalable structure, was standardized by ITU-T. The input sampling rate is 16 kHz.{{citation |publisher=ITU-T |date=2012 |url=http://www.itu.int/rec/T-REC-G.711.1/en |title=G.711.1 : Wideband embedded extension for G.711 pulse code modulation |access-date=2022-12-24}}

Modern speech compression

Much of the later work in speech compression was motivated by military research into digital communications for secure military radios, where very low data rates were used to achieve effective operation in a hostile radio environment. At the same time, far more processing power was available, in the form of VLSI circuits, than was available for earlier compression techniques. As a result, modern speech compression algorithms could use far more complex techniques than were available in the 1960s to achieve far higher compression ratios.

The most widely used speech coding algorithms are based on linear predictive coding (LPC).{{cite journal |last1=Gupta |first1=Shipra |title=Application of MFCC in Text Independent Speaker Recognition |journal=International Journal of Advanced Research in Computer Science and Software Engineering |date=May 2016 |volume=6 |issue=5 |pages=805–810 (806) |s2cid=212485331 |issn=2277-128X |url=https://pdfs.semanticscholar.org/2aa9/c2971342e8b0b1a0714938f39c406f258477.pdf |archive-url=https://web.archive.org/web/20191018231621/https://pdfs.semanticscholar.org/2aa9/c2971342e8b0b1a0714938f39c406f258477.pdf |url-status=dead |archive-date=2019-10-18 |access-date=18 October 2019}} In particular, the most common speech coding scheme is the LPC-based code-excited linear prediction (CELP) coding, which is used for example in the GSM standard. In CELP, the modeling is divided in two stages, a linear predictive stage that models the spectral envelope and a code-book-based model of the residual of the linear predictive model. In CELP, linear prediction coefficients (LPC) are computed and quantized, usually as line spectral pairs (LSPs). In addition to the actual speech coding of the signal, it is often necessary to use channel coding for transmission, to avoid losses due to transmission errors. In order to get the best overall coding results, speech coding and channel coding methods are chosen in pairs, with the more important bits in the speech data stream protected by more robust channel coding.

The modified discrete cosine transform (MDCT) is used in the LD-MDCT technique used by the AAC-LD format introduced in 1999.{{cite conference |last1=Schnell|first1=Markus |last2=Schmidt |first2=Markus |last3=Jander |first3=Manuel |last4=Albert |first4=Tobias |last5=Geiger |first5=Ralf |last6=Ruoppila |first6=Vesa |last7=Ekstrand |first7=Per |last8=Bernhard |first8=Grill |date=October 2008 |title=MPEG-4 Enhanced Low Delay AAC - A New Standard for High Quality Communication |url=https://www.iis.fraunhofer.de/content/dam/iis/de/doc/ame/conference/AES-125-Convention_AAC-ELD-NewStandardForHighQualityCommunication_AES7503.pdf |conference=125th AES Convention |publisher=Audio Engineering Society |access-date=20 October 2019 |website=Fraunhofer IIS}} MDCT has since been widely adopted in voice-over-IP (VoIP) applications, such as the G.729.1 wideband audio codec introduced in 2006,{{cite book |last1=Nagireddi |first1=Sivannarayana |title=VoIP Voice and Fax Signal Processing |date=2008 |publisher=John Wiley & Sons |isbn=9780470377864 |page=69 |url=https://books.google.com/books?id=5AneeZFE71MC&pg=PA69}} Apple's FaceTime (using AAC-LD) introduced in 2010,{{cite web|url=http://www.appleinsider.com/articles/10/06/08/inside_iphone_4_facetime_video_calling.html|date=June 8, 2010|access-date=June 9, 2010|title=Inside iPhone 4: FaceTime video calling|publisher=AppleInsider|author=Daniel Eran Dilger}} and the CELT codec introduced in 2011.[http://people.xiph.org/~greg/video/linux_conf_au_CELT_2.ogv Presentation of the CELT codec] {{Webarchive|url=https://web.archive.org/web/20110807182250/http://people.xiph.org/~greg/video/linux_conf_au_CELT_2.ogv |date=2011-08-07 }} by Timothy B. Terriberry (65 minutes of video, see also [http://www.celt-codec.org/presentations/misc/lca-celt.pdf presentation slides] in PDF)

Opus is a free software audio coder. It combines the speech-oriented LPC-based SILK algorithm and the lower-latency MDCT-based CELT algorithm, switching between or combining them as needed for maximal efficiency.{{cite web |url = https://opus-codec.org/ |title=Opus Codec |work=Opus |publisher=Xiph.org Foundation |type=Home page |access-date=July 31, 2012 }}{{cite conference |last1=Valin |first1=Jean-Marc |last2=Maxwell |first2=Gregory |last3=Terriberry |first3=Timothy B. |last4=Vos |first4=Koen |title=High-Quality, Low-Delay Music Coding in the Opus Codec |conference=135th AES Convention |publisher=Audio Engineering Society |date=October 2013 |arxiv=1602.04845 }} It is widely used for VoIP calls in WhatsApp.{{cite news |last1=Leyden |first1=John |title=WhatsApp laid bare: Info-sucking app's innards probed |url=https://www.theregister.co.uk/2015/10/27/whatsapp_forensic_analysis/ |access-date=19 October 2019 |work=The Register |date=27 October 2015}}{{cite book |last1=Hazra |first1=Sudip |last2=Mateti |first2=Prabhaker |chapter=Challenges in Android Forensics |editor-last1=Thampi |editor-first1=Sabu M. |editor-last2=Pérez |editor-first2=Gregorio Martínez |editor-last3=Westphall |editor-first3=Carlos Becker |editor-last4=Hu |editor-first4=Jiankun |editor-last5=Fan |editor-first5=Chun I. |editor-last6=Mármol |editor-first6=Félix Gómez |title=Security in Computing and Communications: 5th International Symposium, SSCC 2017 |date=September 13–16, 2017 |publisher=Springer |isbn=9789811068980 |pages=286–299 (290) |doi=10.1007/978-981-10-6898-0_24 |chapter-url=https://books.google.com/books?id=1u09DwAAQBAJ&pg=PA290}}{{cite book |last1=Srivastava |first1=Saurabh Ranjan |last2=Dube |first2=Sachin |last3=Shrivastaya |first3=Gulshan |last4=Sharma |first4=Kavita |chapter=Smartphone Triggered Security Challenges: Issues, Case Studies and Prevention |editor-last1=Le |editor-first1=Dac-Nhuong |editor-last2=Kumar |editor-first2=Raghvendra |editor-last3=Mishra |editor-first3=Brojo Kishore |editor-last4=Chatterjee |editor-first4=Jyotir Moy |editor-last5=Khari |editor-first5=Manju |title=Cyber Security in Parallel and Distributed Computing: Concepts, Techniques, Applications and Case Studies |date=2019 |publisher=John Wiley & Sons |isbn=9781119488057 |pages=187–206 (200) |doi=10.1002/9781119488330.ch12 |s2cid=214034702 |chapter-url=https://books.google.com/books?id=FzGtDwAAQBAJ&pg=PA200}} The PlayStation 4 video game console also uses Opus for its PlayStation Network system party chat.{{cite web|url=https://doc.dl.playstation.net/doc/ps4-oss/ |title=Open Source Software used in PlayStation4 |publisher=Sony Interactive Entertainment Inc. |access-date=2017-12-11}}{{failed verification|reason=Source does not indicate how Opus is used|date=September 2022}}

A number of codecs with even lower bit rates have been demonstrated. Codec2, which operates at bit rates as low as {{nowrap|450 bit/s}}, sees use in amateur radio.{{cite web |title=GitHub - Codec2 |website=GitHub |date=November 2019 |url=https://github.com/x893/codec2}} NATO currently uses MELPe, offering intelligible speech at {{nowrap|600 bit/s}} and below.Alan McCree, “A scalable phonetic vocoder framework using joint predictive vector quantization of MELP parameters,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 2006, pp. I 705–708, Toulouse, France Neural vocoder approaches have also emerged: Lyra by Google gives an "almost eerie" quality at {{nowrap|3 kbit/s}}.{{Cite web |last=Buckley |first=Ian |date=2021-04-08 |title=Google Makes Its Lyra Low Bitrate Speech Codec Public |url=https://www.makeuseof.com/google-lyra-speech-codec-public/ |access-date=2022-07-21 |website=MakeUseOf |language=en-US}} Microsoft's Satin also uses machine learning, but uses a higher tunable bitrate and is wideband.{{Cite web |last=Levent-Levi |first=Tsahi |date=2021-04-19 |title=Lyra, Satin and the future of voice codecs in WebRTC |url=https://bloggeek.me/lyra-satin-webrtc-voice-codecs/ |access-date=2022-07-21 |website=BlogGeek.me |language=en-US}}

=Sub-fields=

; Wideband audio coding

Linear predictive coding (LPC)
AMR-WB for WCDMA networks
VMR-WB for CDMA2000 networks
Speex, IP-MR, SILK (part of Opus), and USAC/xHE-AAC for VoIP and videoconferencing
Modified discrete cosine transform (MDCT)
AAC-LD, G.722.1, G.729.1, CELT and Opus for VoIP and videoconferencing
Adaptive differential pulse-code modulation (ADPCM)
G.722 for VoIP
Neural speech coding
Lyra (Google): V1 uses neural network reconstruction of log-mel spectrogram; V2 is an end-to-end autoencoder.
Satin (Microsoft)
LPCNet (Mozilla, Xiph): neural network reconstruction of LPC features{{cite web |title=LPCNet: Efficient neural speech synthesis |url=https://github.com/xiph/LPCNet |publisher=Xiph.Org Foundation |date=8 August 2023}}

; Narrowband audio coding

LPC
FNBDT for military applications
SMV for CDMA networks
Full Rate, Half Rate, EFR and AMR for GSM networks
G.723.1, G.728, G.729, G.729.1 and iLBC for VoIP or videoconferencing
ADPCM
G.726 for VoIP
Multi-Band Excitation (MBE)
AMBE+ for digital mobile radio and satellite phone
Codec 2

References

External links

[http://www.itu.int/net/itu-t/sigdb/genaudio/Pseries.htm ITU-T Test Signals for Telecommunication Systems Test Samples]
[http://www.itu.int/rec/T-REC-P.862/ ITU-T Perceptual evaluation of speech quality (PESQ) tool Sources]

Category:Data compression