Speech coding techniques and challenges: a comprehensive literature survey | Multimedia Tools and Applications Skip to main content
Log in

Speech coding techniques and challenges: a comprehensive literature survey

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Speech coding is the process of compressing speech signals for transmission and storage in communication systems. In recent years, speech coding has become increasingly important due to the growing demand for low bitrate communication systems. This paper presents a comprehensive literature survey of speech coding techniques, their importance, and the challenges associated with their implementation. We also discuss the use of speech enhancement techniques in speech coding. The survey covers various speech coding techniques and their limitations in adverse conditions. We highlight the potential of machine learning-based methods in improving speech quality and intelligibility in speech coding systems. Further, metrics for evaluating the performance of speech coding algorithms are highlighted. The survey also discusses the key issues and challenges associated with speech coding, including the trade-off between speech quality and bit rate, and the impact of background noise on speech quality. Further it also covers popular speech databases used in coding research. Our findings provide valuable insights for researchers and practitioners working in speech coding and demonstrate the importance of speech enhancement techniques for improving speech quality and intelligibility in low bitrate communication systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Nagaraja BG, Jayanna HS (2012) Mono and cross lingual speaker identification with the constraint of limited data. IEEE International Conference on Pattern Recognition, Informatics and Medical Engineering 439–443

  2. Spanias AS (1994) Speech coding: A tutorial review. Proc IEEE 82(10):1541–1582

    Google Scholar 

  3. Flanagan JL, Atal BS, Crochiere RE, Jayant NS, Schroeder MR, Tribolet JM (1979) Speech coding. IEEE Trans Commun 27:710–737

    Google Scholar 

  4. Makhoul J, Roucos S, Gish H (1985) Vector quantization in speech coding. Proc IEEE 73(11):1551–1588

    Google Scholar 

  5. Gibson JD (2005) Speech coding methods, standards, and applications. IEEE Circuits and Systems Magazine 5(4):30–49

    Google Scholar 

  6. Atal BS, Cuperman V, Gersho A (1991) Advances in speech coding. Springer Science & Business Media 114

  7. Goldberg R (2019) A practical handbook of speech coders. CRC Press

    Google Scholar 

  8. Jainar SJ, Sale PL, Nagaraja BG (2020) VAD, feature extraction and modelling techniques for speaker recognition: a review. International Journal of Signal and Imaging Systems Engineering 12(1–2):1–18

    Google Scholar 

  9. Nagaraja BG, Jayanna HS (2016) Feature extraction and modelling techniques for multilingual speaker recognition: a review. International Journal of Signal and Imaging Systems Engineering 9(2):67–78

    Google Scholar 

  10. Wang Z, Du Y, Wei K, Han K, Xu X, Wei G, Tong W, Zhu P, Ma J, Wang J, Wang G (2022) Vision, application scenarios, and key technology trends for 6G mobile communications. Science China Information Sciences 65(5):151301

    Google Scholar 

  11. Huth ME, Boschung RL, Caversaccio MD, Wimmer W, Georgios M (2022) The effect of internet telephony and a cochlear implant accessory on mobile phone speech comprehension in cochlear implant users. European archives of oto-rhino-laryngology 279(12):5547–5554

    PubMed  PubMed Central  Google Scholar 

  12. Asfar NA (2022) The implementation of the forensic method using voice recognition technique to analyze voice resemblance towards mobile phone’s voice recorder. International Journal of Forensic Linguistic 3(1):98–104

    Google Scholar 

  13. Park NI, Lim SH, Byun JS, Kim JH, Lee JW, Chun C, Kim Y, Jeon OY (2023) Forensic authentication method for audio recordings generated by voice recorder application on Samsung Galaxy Watch4 series. J Forensic Sci 68(1):139–153

    PubMed  Google Scholar 

  14. Bonny T, Nassan WA, Baba A (2023) Voice encryption using a unified hyper-chaotic system. Multimedia Tools and Applications 82(1):1067–1085

    Google Scholar 

  15. Barbier L, Mbuaki A, Simoens S, Declerck P, Vulto AG, Huys I (2022) Regulatory information and guidance on biosimilars and their use across Europe: a call for strengthened one voice messaging. Frontiers in Medicine 9

  16. Hameed AS (2021) Speech compression and encryption based on discrete wavelet transform and chaotic signals. Multimedia Tools and Applications 80(9):13663–13676

    ADS  Google Scholar 

  17. Yang H, Zhen K, Beack S, Kim M (2021) Source-aware neural speech coding for noisy speech compression. In ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing, p 706–710

  18. Kleijn WB, Storus A, Chinen M, Denton T, Lim FS, Luebs A, Skoglund J, Yeh H (2021) Generative speech coding with predictive variance regularization. IEEE International Conference on Acoustics, Speech and Signal Processing 6478–6482

  19. Casebeer J, Vale V, Isik U, Valin JM, Giri R, Krishnaswamy A (2021) Enhancing into the codec: Noise robust speech coding with vector-quantized autoencoders. IEEE International Conference on Acoustics, Speech and Signal Processing 711–715

  20. Gupta K, Korse S, Edler B, Fuchs G (2022) A DNN based post-filter to enhance the quality of coded speech in MDCT Domain. IEEE ICASSP 836–840

  21. Ding Y, Yu X (2023) A Hybrid Structure Speech coding scheme based on MELPe and LPCNet. IEEE International Conference on Electrical Engineering, Big Data and Algorithms 809–812

  22. Mustafa A, Büthe J, Korse S, Gupta K, Fuchs G, Pia N (2021) A streamwise GAN vocoder for wideband speech coding at very low bit rate. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics 66–70

  23. Hwang S, Lee E, Jang I, Shin JW (2022) Alias-and-Separate: wideband speech coding using sub-Nyquist sampling and speech separation. IEEE Signal Processing Letters 29:2003–2007

    ADS  Google Scholar 

  24. Lotfidereshgi R, Gournay P (2022) Cognitive coding of speech. IEEE ICASSP 7772–7776

  25. Korse S, Gupta K, Fuchs S (2020) Enhancement of coded speech using a mask-based post-filter. IEEE ICASSP 6764–6768

  26. Roccetti M, Ghini V, Pau G, Salomoni P, Bonfigli ME (2001) Design and experimental evaluation of an adaptive playout delay control mechanism for packetized audio for use over the internet. Multimedia Tools and Applications 14:23–53

    Google Scholar 

  27. Moon S, Kurose J, Towsley D (1998) Packet audio playout delay adjustment: performance bounds and algorithms. Multimedia Systems 6:17–28

    Google Scholar 

  28. Thimmaraja YG, Nagaraja BG, Jayanna HS (2021) Speech enhancement and encoding by combining SS-VAD and LPC. International Journal of Speech Technology 24:165–172

    Google Scholar 

  29. Ghinea G, Angelides MC (2004) A user perspective of quality of service in m-commerce. Multimedia Tools and Applications 22:187–206

    Google Scholar 

  30. Das N, Chakraborty S, Chaki J, Padhy N, Dey N (2021) Fundamentals, present and future perspectives of speech enhancement. International Journal of Speech Technology 24:883–901

    Google Scholar 

  31. Yadava TG, Nagaraja BG, Jayanna HS (2022) A spatial procedure to spectral subtraction for speech enhancement. Multimedia Tools and Applications 81(17):23633–23647

    Google Scholar 

  32. Yadava TG, Jayanna HS (2019) Speech enhancement by combining spectral subtraction and minimum mean square error-spectrum power estimator based on zero crossing. International Journal of Speech Technology 22:639–648

    Google Scholar 

  33. Cui X, Chen Z, Yin F (2020) Speech enhancement based on simple recurrent unit network. Appl Acoust 157:107019

    Google Scholar 

  34. Yadava TG, Nagaraja BG, Jayanna HS (2022) Enhancements in continuous Kannada ASR system by background noise elimination. Circuits, Systems, and Signal Processing 41(7):4041–4067

    Google Scholar 

  35. Yechuri S, Vanambathina S (2023) A nested U-net with efficient channel attention and d3net for speech enhancement. Circuits, Systems, and Signal Processing 1–21

  36. Bie X, Leglaive S, Alameda-Pineda X, Girin L (2022) Unsupervised speech enhancement using dynamical variational autoencoders. IEEE/ACM Transactions on Audio, Speech, and Language Processing 30:2993–3007

    Google Scholar 

  37. Casebeer J, Vale V, Isik U, Valin JM, Giri R, Krishnaswamy A (2021) Enhancing into the codec: Noise robust speech coding with vector-quantized autoencoders. IEEE ICASSP 711–715

  38. Rezki M, Ayad M (2022) A synthetic sleep snoring study through the use of linear predictive speech techniques. In 2022 19th International Multi-Conference on Systems, Signals & Devices, p 896–899

  39. Nagaraja BG, Jayanna HS (2012) Multilingual speaker identification with the constraint of limited data using multitaper MFCC. Proc. International Conference on Recent Trends in Computer Networks and Distributed Systems Security 127–134

  40. Bhatia S, Kumar A, Reddy T, Varshney N, Basheer S (2023) Matrix quantization and LPC vocoder based linear predictive for low-resource speech recognition system. ACM Transactions on Asian and Low-Resource Language Information Processing

  41. Sankar MA, Sathidevi PS (2023) A wideband scalable bit rate mixed excitation linear prediction-enhanced speech coder by preserving speaker-specific features. Circuits, Systems, Signal Processing 1–27

  42. Al-Heeti MM, Hammad JA, Mustafa AS (2022) Voice encoding for wireless communication based on LPC, RPE, CELP, International Congress on Human-Computer Interaction. Optimization and Robotic Applications 1–4

  43. Wang L, Belina J, Vasinonta A, Berner M, Ramprashad S (1994) Compression of ECG using a code excited linear prediction (CELP). International Conference of the IEEE Engineering in Medicine and Biology Society 2:1264–1265

    Google Scholar 

  44. Zaki FW (1991) Sequentially adaptive differential pulse code modulation using adaptive LSP filters. MEJ, Mansoura Engineering Journal 16(2):1–18

    Google Scholar 

  45. He Y (2021) Exploring adaptive differential pulse-code modulation towards resource-efficient full-spectrum wireless neural recording (Doctoral dissertation, State University of New York at Binghamton)

  46. Sadeeq MA, Abdulazeez AM (2020) Neural networks architectures design, and applications: A review. In 2020 International Conference on Advanced Science and Engineering p 199–204

  47. Alam M, Samad MD, Vidyaratne L, Glandon A, Iftekharuddin KM (2020) Survey on deep neural networks in speech and vision systems. Neurocomputing 417:302–321

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Siniscalchi SM, Svendsen T, Lee CH, CH, (2014) An artificial neural network approach to automatic speech processing. Neurocomputing 140:326–338

  49. Chen Y, Mukherjee D, Han J, Grange A, Xu Y, Parker S, Chen C, Su H, Joshi U, Chiang CH, Wang Y (2020) An overview of coding tools in AV1: The first video codec from the alliance for open media. APSIPA Transactions on Signal and Information Processing 9:e6

    Google Scholar 

  50. Moriya T, Honda M (1987) Transform coding of speech with weighted vector quantization. IEEE ICASSP’87 1629–1632

  51. Shlomot E, Cuperman V, Gersho A (1997) Hybrid coding of speech at 4 kbps, IEEE Workshop on Speech Coding for Telecommunications Proceedings. Attacking Fundamental Problems in Speech Coding, Back to Basics, pp 37–38

    Google Scholar 

  52. Shlomot E, Cuperman V, Gersho A (1998) Combined harmonic and waveform coding of speech at low bit rates. IEEE ICASSP ’98 (Cat. No.98CH36181) 2:585–588

  53. Klejsa J, Hedelin P, Zhou C, Fejgin R, Villemoes L (2019) High-quality speech coding with sample RNN. In ICASSP IEEE International Conference on Acoustics, Speech and Signal Processing, p 7155–7159

  54. Hu X, Li G, Xia X, Lo D, Jin Z (2020) Deep code comment generation with hybrid lexical and syntactical information. Empirical Software Engineering 25:2179–2217

    Google Scholar 

  55. Bhangale KB, Mohanaprasad K (2021) A review on speech processing using machine learning paradigm. International Journal of Speech Technology 24:367–388

    Google Scholar 

  56. Arias-Vergara T, Klumpp P, Vasquez-Correa JC, Nöth E, Orozco-Arroyave JR, Schuster M (2021) Multi-channel spectrograms for speech processing applications using deep learning methods. Pattern Anal Applic 24:423–431

    Google Scholar 

  57. Rix AW, Beerends JG, Hollier MP, Hekstra AP (2001) Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. IEEE international conference on acoustics, speech, and signal processing, Proceedings (Cat. No. 01CH37221) 2:749–752

  58. Streijl RC, Winkler S, Hands DS (2016) Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives. Multimedia Systems 22(2):213–227

    Google Scholar 

  59. Chen F, Hu YI (2013) Modifying the normalized covariance metric measure to account ratio 54:503–515

    Google Scholar 

  60. Saleem N, Khattak MI, Nawaz A, Umer F, Ochani MK (2021) Perceptually weighted \(\beta \)-order spectral amplitude Bayesian estimator for phase compensated speech enhancement. Applied Acoustics 178:108007

    Google Scholar 

  61. Hedelin P, Nordén F, Skoglund J (1999) SD optimization of spectral coders. IEEE Workshop on Speech Coding Proceedings, Model, Coders, Error Criteria (Cat. No. 99EX351) 28–30

  62. Zue V, Seneff S, Glass J (1990) Speech database development at MIT: TIMIT and beyond. Speech communication 9(4):351–356

    Google Scholar 

  63. Sharma S, Ellis D, Kajarekar S, Jain P, Hermansky H (2000) Feature extraction using non-linear transformation for robust speech recognition on the Aurora database. IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings (Cat. No. 00CH37100) 2:II1117–II1120

  64. Hu Y, Loizou P (2008) Evaluation of objective quality measures for speech enhancement. IEEE Transactions on Speech and Audio Processing 16(1):229–238

    Google Scholar 

  65. Ma J, Hu Y, Loizou P (2009) Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions. J Acoust Soc Am 125(5):3387–3405

    ADS  PubMed  PubMed Central  Google Scholar 

  66. Veaux C, Yamagishi J, King S (2013) The voice bank corpus: Design, collection and data analysis of a large regional accent speech database 6709856. https://doi.org/10.1109/ICSDA

  67. Robinson T, Fransen J, Pye D, Foote J, Renals S (1995) WSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition. International Conference on Acoustics, Speech, and Signal Processing 1:81–84

    Google Scholar 

  68. Elenius K, Lindberg J (1997) SpeechDat - speech databases for creation of voice driven teleservices 4:61–64

    Google Scholar 

  69. Hirsch HG, Pearce D (2000) The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In ASR2000-Automatic speech recognition: challenges for the new Millenium ISCA tutorial and research workshop

  70. Panayotov V, Chen G, Povey D, Khudanpur S (2015) Librispeech: an asr corpus based on public domain audio books. IEEE international conference on acoustics, speech and signal processing 5206–5210

  71. Du J, Tu YH, Sun L, Ma F, Wang HK, Pan J, Liu C, Chen JD, Lee CH (2016) The USTC-iFlytek system for CHiME-4 challenge. Proc. CHiME 4:36–38

    Google Scholar 

  72. Chen SJ, Xia W, Hansen JH (2021) Scenario aware speech recognition: Advancements for apollo fearless steps & CHiME-4 corpora. IEEE Automatic Speech Recognition and Understanding Workshop 289–295

  73. Zamyatnin AA, Borchikov AS, Vladimirov MG, Voronina OL (2006) The EROP-Moscow oligopeptide database. Nucleic Acids Res 34(suppl_1):D261–D266

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thimmaraja Yadava G.

Ethics declarations

Conflicts of interest

The authors have no conflict of interests on the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mohamed Anees and Thimmaraja Yadava G contributed equally to this work.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

G, N.B., Anees, M. & G, T.Y. Speech coding techniques and challenges: a comprehensive literature survey. Multimed Tools Appl 83, 29859–29879 (2024). https://doi.org/10.1007/s11042-023-16665-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16665-3

Keywords

Navigation