You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review 
Chapter Title:  LP Residual Enhancement 
1. Abstract of Cited Paper (BIB001):  This paper proposes an approach for processing speech from multiple microphones to enhance speech degraded by noise and reverberation. The approach is based on exploiting the features of the excitation source in speech production. In particular, the characteristics of voiced speech can be used to derive a coherently added signal from the linear prediction (LP) residuals of the degraded speech data from different microphones. A weight function is derived from the coherently added signal. For coherent addition the time-delay between a pair of microphones is estimated using the knowledge of the source information present in the LP residual. The enhanced speech is generated by exciting the time varying all-pole filter with the weighted LP residual. 
2. Abstract of Cited Paper (BIB002):  In this paper a method based on the excitation source information is proposed for enhancement of speech, degraded by speech from other speakers. Speech from multiple speakers is simultaneously collected over two spatially distributed microphones. Time-delay of each speaker with respect to the two microphones is estimated using the excitation source information. A weight function is derived for each speaker using the knowledge of the timedelay and the excitation source information. Linear prediction (LP) residuals of the microphone signals are processed separately using the weight functions. Speech signals are synthesized from the modified residuals. One speech signal per speaker is derived from each microphone signal. The synthesized speech signals of each speaker are combined to produce enhanced speech. Significant enhancement of the speech of one speaker relative to other was observed from the combined signal. 
3. Abstract of Cited Paper (BIB003):  Abstract A new algorithm for the enhancement of speech corrupted by additive noise is proposed. This algorithm estimates the linear prediction residuals of the clean speech using a constrained optimization criterion. The signal distortion is minimized in the residual domain subject to a constraint on the average power of the noise residuals. Enhanced speech is obtained by exciting the time-varying all-pole synthesis filter with the estimated residuals of the clean speech. The proposed method was tested with speech corrupted by both white Gaussian and colored noise. The enhancement performances were evaluated in terms of segmental signal-to-noise ratio (SNR) and ITU-PESQ scores. Experimental results indicate our method yields better enhancement results than a former residual-weighting scheme [Yegnanarayana, B., Avendano, C., Hermansky, H., Murthy P.S., 1999. Speech enhancement using linear prediction residual. Speech Commun. 28, 25–42]. The proposed method also achieves better noise reduction than the time-domain subspace method [Ephraim, Y., Van Trees, H.L., 1995. A signal subspace approach for speech enhancement. IEEE Trans. Speech Audio Process. 3, 251–266] on real world colored noise. 
4. Abstract of Cited Paper (BIB004):  We present a reverberant speech enhancement algorithm which, operating on the linear prediction residual of spatially averaged multi- microphone observations, utilizes temporal averaging of neighbouring larynx cycles. The enhanced larynx cycles are used to design an equalization filter, which is applied in order to dereverberate both voiced and unvoiced speech. The DYPSA algorithm is employed and evaluated for larynx cycle segmentation of reverberant speech. Simulation results show reverberation reduction with up to 5 dB in terms of segmental signal-to-reverberant ratio and 0.34 in terms of normalized Bark spectral distortion score.

Literature Review:
Most of the studies on the speech enhancement discussed above focus on enhancement, based on suppression of noise. These methods disturb the spectral balance in speech, resulting in unpleasant distortions in the enhanced speech. Yegnanarayana et al. proposed a noisy speech enhancement method by exploiting the characteristics of excitation source signal such as the LP residual [19] . The basic approach for speech enhancement is to identify the high SNR portions in the noisy speech signal, and enhance those portions relative to the low SNR portions, without causing significant distortion in the enhanced speech. The residual signal samples are multiplied with the weight function, and the modified residual is used to excite the time-varying all-pole filter derived from the given noisy speech, to generate the enhanced speech. In this method, enhancement is carried out by the following three steps [19] : (i) identification and enhancement of high SNR regions at the gross level; (ii) identification and enhancement of high SNR regions at the fine level; and, (iii) enhancement of spectral peaks over valleys. In BIB004 , a speech enhancement algorithm, similar to [19] , has been proposed. It differs with the former residual weighting scheme in that the weights on the LP residuals have been derived, based on a constrained optimization criterion. Enhanced speech is obtained by exciting the time-varying all-pole synthesis filter with the enhanced residual. In BIB002 , the authors exploited the use of coherently added Hilbert envelope (HE) for LP residual reconstruction. Large amplitude at the instant of strong excitation, a feature of HE, makes it a good indicator of glottal closure (GC), where an excitation pulse takes place. Therefore, applying HE to the LP residual as a weighting function has the effect of emphasizing the pulse train structure for voiced speech, which leads to an enhanced LP residual signal. Yegnanarayana and Murthy developed a reverberant speech enhancement system by manipulating excitation source information based on the residual characteristics of speech BIB001 . Manipulation of the residual signal is more appropriate than the manipulation of speech signal, especially for short segments, as the residual signal samples are generally less correlated than the speech samples. On the other hand, for manipulation of the speech signal directly, the choice of the size and shape of the window may affect the results significantly. The processing method involves identifying and manipulating the linear prediction residual signal in different regions of the reverberant speech signal, namely, regions in which there is high SRR, low SRR and reverberant component only. Generally, there will be changes in the excitation characteristics both at the fine and gross levels, during speech production. The fine level changes may be from closed phase to open phase in a pitch period and the gross level changes may be from silence to voiced excitation. The weight function for the excitation source signal is derived at two different levels, namely, gross and fine levels to obtain the enhanced signal. The gross level weight function is derived to identify the high SRR and low SRR regions of the reverberant speech and the fine level weight function is derived to enhance the instants of significant excitation of original signal. In BIB001 , gross level identification is done using the entropy of the distribution of the samples in the LP residual signal and fine level identification is done using the normalized prediction error. The authors also observed that there was a reduction in the flatness of the spectral envelope, owing to reverberation. Thus, the LP coefficients are manipulated to increase the spectral flatness. Finally, the enhanced speech signal is resynthesized from the processed LP residual signal and the coefficients. In BIB005 , the authors proposed a multi-channel speech enhancement technique by exploiting the features of the excitation source in speech production. The Hilbert envelope (HE) of LP residual was used to derive the information of the strength of excitation. A weight function was derived by coherently combining the delay compensated HEs of the LP residual signals from the different microphones. The enhanced speech was again obtained by exciting the time-varying all-pole filter with the LP residual modified by the weight function. In [37] , the authors presented a spatiotemporal averaging method for the enhancement of reverberant speech. The basis was that the waveform of the LP residual between adjacent larynx-cycles varied slowly, so that each such cycle could be replaced by an average of itself and its nearest neighboring cycle. The averaging resulted in the suppression of spurious peaks in the LP residual, caused by room reverberation. Finally, a speech signal with reduced reverberation was synthesized with the enhanced LP residual. Most of the LP residual techniques rely on the important assumption that the calculated LP coefficients of the all-pole filter are unaffected by the multi-path reflections of the room. Gaubitch and Naylor showed that this assumption holds only in a spatially averaged sense, and that it cannot be guaranteed at a single point in space for a given room [38] . Recently, Gaubitch et al. used statistical room acoustic theory for the analysis of the auto regressive (AR) modeling of reverberant speech  . They investigated and showed that proper calculation of the LP coefficients, i.e., using spatially averaged LP coefficients, improved the quality of LP residual enhancement techniques. A method for processing speech from a multi-speaker environment, using excitation source information, is proposed by the authors in BIB003 . The speech of each speaker is enhanced with respect to the speech of the other, by performing the relative emphasis of speech signal around each instant of significant excitation of the desired speaker. The relative emphasis is achieved by giving a larger weight to the LP residual samples in the region, around the instants of significant excitation, and lower weight to the samples in the other regions BIB003 . The temporal processing approach proposed in BIB003 composes of following steps: (i) Identification of instants of significant excitation for determining the short high energy regions corresponding to each speaker, (ii) Classification of extracted instants into two speaker classes, (iii) Weighting the LP residual to enhance the excitation characteristics of desired speaker, and (iv) Synthesizing the enhanced speech by exciting the time-varying all-pole filter with the LP residual modified by the weight function. The HE of LP residual is used as a representation for the sequence of impulses corresponding to the instants of significant excitation of the vocal tract system BIB003 . When these sequences are added coherently, using the knowledge of the time-delay of each speaker, the strengths of the excitation of the desired speaker are enhanced, relative to the strengths of excitation of other speakers. From the coherently added sequence of impulses, a weight function is derived, which is used to derive a modified excitation signal. This modified excitation signal is used to synthesize speech using the vocal-tract system characteristics derived from the degraded speech.