# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 2 Speech-Coding Background  
In the past few decades, speech-coding research and development has been dominated by efforts to standardize speech codecs for different applications [6,7]. Initial applications were for narrowband (NB) or telephone bandwidth speech, which refers to speech occupying the band 300 Hz to 3400 Hz, and were for wireline telephony in the circuit-switched digital telephone network and then the emphasis turned to digital cellular communications. Starting with G.729, a NB speech codec operating at 8 kbps [8], the primary approach to speech-coding since has been analysis-by-synthesis (AbS) coding using a codebook excitation, called Code-Excited Linear Prediction (CELP). This codec produced unbarred of performance at that design rate. Interest then turned to developing codecs for wideband (WB) speech, which occupies the band 50 Hz to 7 kHz, to allow sharper sounding and more intelligible speech than possible with NB speech. This effort led to the establishment of the Adaptive Multirate (AMR) codec that has both narrowband and wideband capabilities and a range of transmitted bit rates [9,10]. More recently, the speech-coding standards activity turned to not only improving the quality of the coded speech at the desired bit rates for both NB and WB speech, but also to incorporating the capability of coding music in wider bands and detecting and compensating for noisy environments. This work led to the standardization of the Enhanced Voice Services (EVS) codec with truly remarkable performance [11]. Despite the availability of the new EVS codec, AMR continues to be the most installed and used codec in the World. AMR-WB is also the currently preferred codec for First Responder voice communications in the U. S. over LTE by _First Net_ because of its widely installed base and the years of reliable performance, which is needed for first responder voice communications. Specific details of the performance, complexity, and latency for many standardized codecs are available in Gibson [6]. An overview of the field of speech-coding standards and speech-coding research, including the relationship between speech and audio coding and future challenges, is presented in [7], wherein extensive key references to the literature are included. These publications point out the leading role played by the AMR-WB and AMR-NB speech codecs in VoIP and digital cellular communications throughout the World. The discussion of the EVS codec points out that this new codec is designed for multi-functionality by providing the ability to code narrowband, wideband, and super wideband speech as well as audio. Both increased front end and output signal processing addresses issues such as background noise in the input and smooth switching between bandwidths and coding modes at the output. This greatly increased functionality comes with a corresponding tremendous increase in complexity and an increase in total latency. Not included in the references [6,7] is a discussion of the application of Deep Learning to speech-coding, which is receiving some research attention. The success of Deep Learning for speech recognition has motivated research into the use of deep learning for speech-coding. Speech-coding is not quite as good a fit for deep neural networks as for speech recognition, for example, since latencies greater than a few tenths of a second can preclude person-to-person voice communications and since the encoding and decoding must be accomplished in real time on mobile handsets (for the primary application). Furthermore, researchers in neural networks and learning also desire, as is the vogue in deep learning applications today, end-to-end speech-coding, meaning speech-coding without any, or at least minimal, domain knowledge.