# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 2 Speech-Coding Background
These research goals are challenging since current speech codecs operate in real time with under a tenth of a second for encoding cascaded with decoding, are implementableon moderate complexity mobile devices, work well for multiple languages, and achieve excellent quality and intelligibility, allowing the listener to not only understand what is being said but also to identify the speaker. There are limits to speech codecs today, but those limits tend to be on the naturalness and intelligibility achievable at lower bit rates such as 4 kilobits/sec and below, and on increased complexity and ever-increasing latency. However, the lure of end-to-end speech-coding without the expertise of a domain expert and trainable in hours or days rather than requiring years for codec design is sufficiently strong that several researchers/research groups have been motivated to approach the problem [12; 13; 14; 15; 16]. The study by [PERSON] [12] on using deep neural networks (DNNs) to design an \"end-to-end\" speech codec captures the challenges faced when designing a speech codec with minimal domain knowledge. First, the term end-to-end is usually intended to mean that the raw signal is input and the DNN does the rest-no domain knowledge required. For speech-coding, all the papers choose a window, a window size, and an overlap of the adjacent windows to package the raw data before passing it on to the DNN. These choices are significant and domain knowledge and experimentation should be used in their selection. The work in [12] used a 32 msec (512 samples at 16,000 samples/s) Hann window with 2 msec overlap (32 samples) for adjacent windows. A greater overlap of the windows would likely produce better results. The speech codec in [12] employed an additional much more domain knowledge assumption since it used mel frequency cepstral coefficients (MFCCs) within a sum of squared error perceptual distortion measure. In the training process for the DNN-based codec, a separate network is trained for each bitrate targeted and each network required about 20 h of training on a GeForce GTX 1080 Ti GPU. Encoding and decoding time is nearly equally split and the average time it takes for encoding and decoding of a 30 msec window using an Intel i7-4970K CPU (3.8 GHz) and the GeForce GTX 1080 Ti GPU is about 26 msec. The conclusion is that the DNN codec can operate in real time, albeit on processors that are far faster than embedded systems or cell phones. The performance of this codec is compared to the AMR-WB codec and achieves some PESQ-MOS values approximately equal to AMR, but the perceptual tests showed that the quality was poorer than AMR. Based on the claims in [12], [PERSON], et al. [13] move toward simplifying what appears to be the primary limitation of that prior work, DNN complexity, by developing a simpler learning approach to achieving similar performance. [PERSON], et al adopt the same window, window size, and overlap, employ the same MFCC perceptual loss function, and focus on the same four AMR-WB bit rates as in [12]. Using CMRL, the authors' algorithms have 40% fewer model parameters than in [12], thus apparently offering a significant reduction in complexity. Three different indicators are used to determine the comparative performance, SNR (signal-to-noise (reconstruction error) ratio), PESQ-MOS [4], and a MUSHRA subjective test [17]. It has been known for decades that SNR has no meaning for analysis-by-synthesis speech codecs such as AMR, so this indicator is a diversion only for the uninitiated. The PESQ-MOS results presented for the raw input speech are similar to those from [12] in that the CMRL has a higher PESQ-MOS for all four rates than AMR-WB and K-Net (the name associated with the Kankanahalli DNN), with a few tenths higher gap over AMR-WB. For the MUSHRA subjective testing, the conclusions change dramatically compared to the objective test results. The CMRL codec performs substantially poorer than AMR-WB at all four rates.