# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 6 Conclusions  
In this paper we proposed a new procedure for the alignment of times series. The proposed method, called TRANSIENCE, can be seen as an extension to the well-known DTW algorithm, but allowing the alignment of pairs of sequences consisting of feature vectors with different dimensionality. To enable this feature, powerful DNNs are used to nonlinearly map the feature vectors from the sequences into a common, latent space where the resulting embedding vectors have the same dimensionality and are maximally similar. Once these embedding vectors are obtained, the DTW algorithm is used to compute the optimum alignment between them. We proposed different versions of this algorithm, mainly affecting the way the DNNs are trained.  
To evaluate the proposed technique, an A2A task involving the synthesis of audible speech from articulatory signals was employed. To this end, articulatory and speech data recorded from healthy speakers for multiple sessions were used. Our algorithm was then used to align the articulatory and speech signals and, once aligned, the aligned signals were employed for training DNNs able to estimate the speech parameters from the articulatory signals; thus, enabling people with speech impairments to communicate again. Our results showed that it is feasible to deploy direct synthesis techniques in nonparallel scenarios. In particular, both the objective and subjective evaluation conducted showed us that the quality of speech signals obtained by our algorithm approaches (when not outperforming) the results achieved when using an oracle alignment technique.  
Regarding future work, we would like to evaluate our technique using different types of biosignals (e.g., EEG) and data obtained from clinical population. Furthermore, even better alignment could be achieved by introducing additional constraints (e.g., phonetic constraints) or, alternatively, using state-of-the-art neural network architectures, such as the ResNET [43] or DenseNET [44] architectures.  
The individual contributions are provided as follows. Contributions: conceptualization, [PERSON]-L., A.G.-A., J.L.P.-C. and P.D.G.; methodology, [PERSON]-L., A.G.-A., J.L.P.-C. and P.D.G.; software, [PERSON]-L., A.G.-A. and J.L.P.-C.; validation, [PERSON]-L. and A.G.-A.; formal analysis, [PERSON]-L., A.G.-A. and P.D.G.; investigation, [PERSON]-L., A.G.-A., J.L.P.-C. and P.D.G.; resources, [PERSON]-[PERSON], [PERSON] and P.D.G.; data curation, [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON], [PERSON], [PERSON] and P.D.G.; visualization, [PERSON]-[PERSON], [PERSON] and [PERSON]; supervision, [PERSON]-L.; project administration, [PERSON]-L. and [PERSON]; funding acquisition, [PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 3: Results of the ABX listening test on speech quality.  
**Funding:** This work was supported in part by the Spanish State Research Agency (SRA) grant number PID2019-108040 RB-C22/SRA/10.13039/501100011033, and the FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades project no. B-SEJ-570-UGR20.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the UK National Research Ethics Service.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical reasons.  
**Acknowledgments:** We thank [PERSON] and [PERSON] from the University of Hull (UK) for their help during data collection.  
**Conflicts of Interest:** The authors declare no conflict of interest.