Speech coding and decoding, also known as speech compression, is the process of reducing the amount of data required to represent a speech signal without compromising its quality. Speech coding is an essential part of modern communication systems, as it enables efficient transmission and storage of speech signals over various networks and devices. Speech coding is used in a wide range of applications, including voice-over-IP (VoIP), mobile communication, satellite communication, digital audio broadcasting, speech recognition, and text-to-speech (TTS) systems. In these applications, speech coding plays a critical role in reducing the amount of data required for transmission, thus reducing bandwidth and storage requirements, improving voice quality, and reducing latency. Moreover, speech coding has become increasingly important with the growing popularity of digital communication and the rise of mobile devices. Overall, speech coding and decoding have become an integral part of modern communication systems, allowing for efficient transmission and storage of speech signals while maintaining high voice quality. Without speech coding, many modern communication systems would not be able to function effectively, making it a vital technology for today's digital world.

There are several speech compression techniques used in speech coding. Here are some of the most common ones:

Techniques	Explanation	Pros	Cons	Application
Waveform Coding	Waveform coding is a type of lossless compression technique that preserves the original waveform of the speech signal. In this technique, the speech signal is sampled at a fixed rate, and the samples are quantized and encoded using various techniques, such as delta modulation and pulse code modulation (PCM). One of the most common waveform coding techniques is the G.711 standard, which is used in traditional telephone networks.	Waveform coding preserves the original waveform of the speech signal, ensuring high-quality speech reproduction.	Waveform coding has a high bit rate and requires a large amount of storage and bandwidth.	Waveform coding is commonly used in traditional telephone networks, where voice quality is critical, and bandwidth is not a significant concern.
Parameter Coding	Parametric coding is a type of lossy compression technique that models the speech signal as a set of parameters. In this technique, the speech signal is analyzed, and its spectral and temporal properties are estimated using various techniques, such as linear predictive coding (LPC), vector quantization (VQ), and code-excited linear prediction (CELP). One of the most common parametric coding techniques is the G.729 standard, which is used in VoIP and mobile networks.	Parametric coding provides high compression ratios, which reduces storage and bandwidth requirements. It is also computationally efficient and requires less power.	Parametric coding introduces some distortion into the speech signal, which can affect the quality of the reproduced speech.	Parametric coding is commonly used in VoIP and mobile networks, where bandwidth and storage are limited, and computational efficiency is crucial.
Hybrid Coding	Hybrid coding is a combination of waveform and parametric coding techniques. In this technique, the speech signal is first encoded using a parametric coding technique, and then the residual signal is encoded using waveform coding techniques. One of the most common hybrid coding techniques is the G.722 standard, which is used in digital audio broadcasting.	Hybrid coding provides high-quality speech reproduction with a lower bit rate than waveform coding.	Hybrid coding is more complex than waveform or parametric coding and requires more computational resources.	Hybrid coding is commonly used in digital audio broadcasting, where high-quality speech reproduction is essential, and bandwidth is limited.

In conclusion, the choice of speech compression technique depends on the application's requirements, including voice quality, bandwidth, storage, and computational resources. While waveform coding provides high-quality speech reproduction, it requires a large amount of storage and bandwidth. Parametric coding provides high compression ratios, making it ideal for bandwidth-limited applications, while hybrid coding provides high-quality speech reproduction with a lower bit rate.

Speech decoding techniques are used to convert the compressed speech signal back to its original form. Here are some of the most common speech decoding techniques used in speech coding:

Techniques	Explanation	Pros	Cons	Application
Waveform Reconstruction	Waveform reconstruction is a technique used in waveform coding. In this technique, the compressed speech signal is decoded using a decoder that reconstructs the original waveform from the quantized samples. The decoded signal is then passed through a reconstruction filter to remove any distortion.	Waveform reconstruction provides high-quality speech reproduction with low distortion.	Waveform reconstruction requires a large amount of storage and bandwidth.	Waveform reconstruction is commonly used in traditional telephone networks, where voice quality is critical, and bandwidth is not a significant concern.
Model-Based Synthesis	Model-based synthesis is a technique used in parametric coding. In this technique, the compressed speech signal is decoded using a decoder that reconstructs the speech signal's parameters. The parameters are then used to synthesize the original speech signal using various synthesis techniques, such as LPC synthesis and CELP synthesis.	Model-based synthesis provides high compression ratios and requires less storage and bandwidth than waveform coding	Model-based synthesis can introduce some distortion into the speech signal, affecting speech quality.	Model-based synthesis is commonly used in VoIP and mobile networks, where bandwidth and storage are limited, and computational efficiency is crucial.
Hybrid Decoding	Hybrid decoding is a technique used in hybrid coding. In this technique, the compressed speech signal is first decoded using model-based synthesis to reconstruct the speech signal's parameters. The residual signal is then reconstructed using waveform reconstruction. The decoded parameters and residual signal are then combined to synthesize the original speech signal.	Hybrid decoding provides high-quality speech reproduction with a lower bit rate than waveform coding.	Hybrid decoding is more complex than waveform or model-based synthesis and requires more computational resources.	Hybrid decoding is commonly used in digital audio broadcasting, where high-quality speech reproduction is essential, and bandwidth is limited.

In conclusion, the choice of speech decoding technique depends on the application's requirements, including voice quality, bandwidth, storage, and computational resources. While waveform reconstruction provides high-quality speech reproduction, it requires a large amount of storage and bandwidth. Model-based synthesis provides high compression ratios, making it ideal for bandwidth-limited applications, while hybrid decoding provides high-quality speech reproduction with a lower bit rate.

Fig 1. Flowchart of the Speech encoding and decoding.

To evaluate the performance of speech coding and decoding, various quality metrics are used to measure the difference between the original speech signal and the reconstructed signal. The most commonly used quality metrics are:

Fig 2. Quality Matrix of the speech encoding and decoding.

These metrics are used to compare different speech coding and decoding techniques by evaluating the performance of each technique in terms of speech quality, compression ratio, computational complexity, and bandwidth requirements. The technique with the highest MOS, SNR, or PESQ score and the lowest MSE score is considered to have the best performance. However, it is essential to consider other factors, such as computational complexity and bandwidth requirements, when selecting a speech coding and decoding technique for a specific application.

Recent advances in speech coding and decoding include deep learning-based techniques, such as convolutional neural networks and recurrent neural networks, which can learn complex speech patterns and generate high-quality speech signals with lower bit rates than traditional techniques. Artificial intelligence and machine learning have also been used to optimize speech coding and decoding algorithms, leading to improved performance in terms of speech quality, compression ratio, and computational efficiency. Other advances include the use of multi-band excitation and spectral parameter estimation techniques, which improve the accuracy of speech coding and decoding. Finally, the development of low-delay speech coding algorithms and the use of network coding techniques have led to improved performance in real-time applications, such as VoIP and video conferencing. 

Fig 3. Separation of two different signal.

Speech coding and decoding techniques are used in a variety of applications, including:

1. Telecommunications: Speech coding and decoding are used in telecommunications for voice-over-IP (VoIP) applications, video conferencing, and mobile communication systems.

2. Multimedia Applications: Speech coding and decoding are used in multimedia applications, such as digital audio and video recording, streaming, and playback.

3. Speech Recognition: Speech coding and decoding are used in speech recognition applications to convert spoken words into text.

4. Assistive Technology: Speech coding and decoding are used in assistive technology for people with speech impairments, such as text-to-speech and speech synthesis systems.

5. Military and Law Enforcement: Speech coding and decoding are used in military and law enforcement applications, such as secure communication systems and voice encryption.

6. Automotive: Speech coding and decoding are used in automotive applications, such as voice-activated GPS navigation systems and hands-free calling.

7. Consumer Electronics: Speech coding and decoding are used in a variety of consumer electronics, including smart speakers, voice assistants, and wearable devices.

In general, speech coding and decoding are used in any application that involves the transmission or processing of speech signals. Speech coding and decoding still face several challenges that need to be addressed in the future, including:

1. Balancing Quality and Compression: The challenge of balancing speech quality with compression ratio remains a key challenge in speech coding and decoding.

2. Processing Speed and Complexity: The computational complexity of some advanced speech coding and decoding techniques can be challenging, especially for real-time applications.

3. Bandwidth Limitations: Limited bandwidth availability can limit the performance of speech coding and decoding techniques, particularly in low-speed or high-latency networks.

4. Cross-Lingual Performance: Speech coding and decoding techniques need to be optimized for different languages and dialects to improve cross-lingual performance

In terms of future directions, there are several promising areas of research, including:

1. Deep Learning-based Techniques: Deep learning-based techniques are expected to continue to play an important role in speech coding and decoding, improving speech quality and compression ratios.

2. Artificial Intelligence and Machine Learning: AI and ML will continue to play an important role in optimizing speech coding and decoding algorithms.

3. Low-Delay Speech Coding: The development of low-delay speech coding algorithms will improve the performance of real-time applications, such as VoIP and video conferencing.

4. Speech Enhancement: Research into speech enhancement techniques, such as noise reduction and dereverberation, will improve the quality of speech signals before they are encoded.

Overall, the future of speech coding and decoding looks promising, with continued advancements in technology and research expected to lead to significant improvements in speech quality, compression ratio, and computational efficiency.