# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 2 Speech-Coding Background
The authors then investigate an approach wherein the linear predictive coding (LPC) model parameters are calculated and used to form the prediction error which is then fed as input to the CMRL at the encoder side. The LPC coefficients are then used with the outputs of the CMRL to synthesize the speech. This approach, which no longer can be called _end-to-end_, does considerably better on the MUSHRA test, outperforming AMR-WB at the higher rates of 19.85 kbit/s and 23.85 kbits/s but still having significantly lower subjective quality than AMR-WB at 8.85 kbits/s and 15.85 kbits/s. Using LPC with CMRL means that the claimed end-to-end learning method has now adopted the same basic model as used in AMR, and so the CMRL is really spending all the remaining learning effort on finding a good excitation for the LPC model. An interesting and important contribution to the application of neural networks (NN) to speech-coding is the contribution by [PERSON] et al. [15] based on the WaveNet generative audio model [18]. This work does not pursue nor claim end-to-end but provides a structure for low bitrate speech-coding by repurposing WaveNet as a generic generative model for speech. WaveNet is a generative audio model based on DNNs that uses learning conditional probabilities to produce natural sounding speech for applications such as text to speech and to produce natural sounding audio. WaveNet also allows other conditioning variables to be incorporated and this feature is exploited to propose and analyze a new approach to low bitrate speech-coding. Both waveform-based and parametric speech coders are described. A general open source 2.4 kbits/s speech coder is used in the parametric coder. This codec uses a 20 msec frame and represents the spectral envelope with line spectral frequencies at 36 bits/frame, pitch at 7 bits/frame, signal power with 5 bits/frame, and voicing level with 2 bits/frame. The voicing level is determined as in the multiband excitation coder [19]. These parameters are extracted at the encoder and sent to the WaveNet decoder where they are used as conditioning variables. The basic parametric codec selected as the baseline codec is designed for narrowband speech but the training of the WaveNet decoder mapped the lower rate conditioning into wideband speech samples. Objective performance was evaluated using the POLQA [20] and subjective evaluations with MUSHRA listening tests. A WaveNet-based waveform codec was also evaluated and compared to AMR-WB at 23.85 kbits/s and other low bitrate codecs. The WaveNet parametric codec operates at 2.4 kbits/s. The parametric WaveNet codec produced an MOS from POLQA of 2.9 compared to a MOS of 4.7 and 4.6 for the WaveNet waveform codec at 42 kbits/s and AMR-WB at 23.85 kbits/s. However, MUSHRA listening tests capture the importance of the effective bandwidth extension and training since the parametric WaveNet codec achieved a score of 70 compared to about 84 for AMR-WB at 23.85 kbits/s and a score of approximately 25 for the best 2.4 kbits/s codec studied (MELP) [21]. There are a few caveats. First, the experimental results for the parametric WaveNet codec did not always allow the speaker to be identified in another comparison test. This is likely due to the quality of the baseline parametric codec. Furthermore, as the authors note, the computational cost of training and running WaveNet is high compared to the conventional standardized speech codecs. A further investigation of generative speech-coding is in [22] which pushes the bitrate down below 4 kbits/s. End-to-end speech-coding using DNNs and related neural networks to date have not yet achieved the quality and intelligibility necessary to compete with the AMR-WB codec particularly when complexity and real time coding requirements are folded in. A new approach to speech-coding combining the NN-based WaveNet generative model with known low bitrate speech codecs is interesting and shows one way forward to using NNs in speech-coding. The use of variational autoencoders is discussed in [14; 23].