Total: 90
It is a common experience of most speakers that the playback of one’s own voice sounds strange. This can be mainly attributed to the missing bone-conducted speech signal that is not present in the playback signal. It was also shown that some phonemes have a high bone-conducted relative to air-conducted sound transmission, which means that the bone-conduction filter is phone-dependent. To achieve such a phone-dependent modeling we train different speaker dependent and speaker adaptive speech conversion systems using airborne and bone-conducted speech data from 8 speakers (5 male, 3 female), which allow for the conversion of airborne speech to bone-conducted speech. The systems are based on Long Short-Term Memory (LSTM) deep neural networks, where the speaker adaptive versions with speaker embedding can be used without bone-conduction signals from the target speaker. Additionally we also used models that apply a global filtering. The different models are then evaluated by an objective error metric and a subjective listening experiment, which show that the LSTM based models outperform the global filters.
Despite the increasing popularity of end-to-end text-to-speech (TTS) systems, the correct grapheme-to-phoneme (G2P) module is still a crucial part of those relying on a phonetic input. In this paper, we, therefore, introduce a T5G2P model, a Text-to-Text Transfer Transformer (T5) neural network model which is able to convert an input text sentence into a phoneme sequence with a high accuracy. The evaluation of our trained T5 model is carried out on English and Czech, since there are different specific properties of G2P, including homograph disambiguation, cross-word assimilation and irregular pronunciation of loanwords. The paper also contains an analysis of a homographs issue in English and offers another approach to Czech phonetic transcription using the detection of pronunciation exceptions.
Neural vocoders are systematically evaluated on homogeneous train and test databases. This kind of evaluation is efficient to compare neural vocoders in their “comfort zone”, yet it hardly reveals their limits towards unseen data during training. To compare their extrapolation capabilities, we introduce a methodology that aims at quantifying the robustness of neural vocoders in synthesising unseen data, by precisely controlling the ranges of seen/unseen data in the training database. By focusing in this study on the pitch (F0) parameter, our methodology involves a careful splitting of a dataset to control which F0 values are seen/unseen during training, followed by both global (utterance) and local (frame) evaluation of vocoders. Comparison of four types of vocoders (autoregressive, sourcefilter, flows, GAN) displays a wide range of behaviour towards unseen input pitch values, including excellent extrapolation (WaveGlow); widely-spread F0 errors (WaveRNN); and systematic generation of the training set median F0 (LPCNet, Parallel WaveGAN). In contrast, fewer differences between vocoders were observed when using homogeneous train and test sets, thus demonstrating the potential and need for such evaluation to better discriminate the neural vocoders abilities to generate out-of-training-range data.
We provide a systematic review of past studies that use multilingual data for text-to-speech (TTS) of low-resource languages (LRLs). We focus on the strategies used by these studies for incorporating multilingual data and how they affect output speech quality. To investigate the difference in output quality between corresponding monolingual and multilingual models, we propose a novel measure to compare this difference across the included studies and their various evaluation metrics. This measure, called the Multilingual Model Effect (MLME), is found to be affected by: acoustic model architecture, the difference ratio of target language data between corresponding multilingual and monolingual experiments, the balance ratio of target language data to total data, and the amount of target language data used. These findings can act as reference for data strategies in future experiments with multilingual TTS models for LRLs. Language family classification, despite being widely used, is not found to be an effective criterion for selecting source languages.
The combination of the recently proposed LPCNet vocoder and a seq-to-seq acoustic model, i.e., Tacotron, has successfully achieved lightweight speech synthesis systems. However, the quality of synthesized speech is often unstable because the precision of the pitch parameters predicted by acoustic models is insufficient, especially for some tonal languages like Chinese and Japanese. In this paper, we propose an end-to-end speech synthesis system, TacoLPCNet, by conditioning LPCNet on Mel spectrogram predictions. First, we extend LPCNet for the Mel spectrogram instead of using explicit pitch information and pitch-related network. Furthermore, we optimize the system by model pruning, multi-frame inference, and increasing frame length, to enable it to meet the conditions required for real-time applications. The objective and subjective evaluation results for various languages show that the proposed system is more stable for tonal languages within the proposed optimization strategies. The experimental results also verify that our model improves synthesis runtime by 3.12 times than that of the baseline on a standard CPU while maintaining naturalness.
Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models. Prosodic speech can be generated by conditioning acoustic features. However, synthesized speech with a large pitch-shift scale suffers from audio quality degradation, and speaker characteristics deformation. To address this problem, we propose a feed-forward Transformer based TTS model that is designed based on the source-filter theory. This model, called FastPitchFormant, has a unique structure that handles text and acoustic features in parallel. With modeling each feature separately, the tendency that the model learns the relationship between two features can be mitigated. Owing to its structural characteristics, FastPitchFormant is robust and accurate for pitch control and generates prosodic speech preserving speaker characteristics. The experimental results show that proposed model outperforms the baseline FastPitch.
This paper presents a speech synthesis method based on deep Gaussian process (DGP) and sequence-to-sequence (Seq2Seq) learning toward high-quality end-to-end speech synthesis. Feed-forward and recurrent models using DGP are known to produce more natural synthetic speech than deep neural networks (DNNs) because of Bayesian learning and kernel regression. However, such DGP models consist of a pipeline architecture of independent models, acoustic and duration models, and require a high level of expertise in text processing. The proposed model is based on Seq2Seq learning, which enables a unified training of acoustic and duration models. The encoder and decoder layers are represented by Gaussian process regressions (GPRs) and the parameters are trained as a Bayesian model. We also propose a self-attention mechanism with Gaussian processes to effectively model character-level input in the encoder. The subjective evaluation results show that the proposed Seq2Seq-SA-DGP can synthesize more natural speech than DNNs with self-attention and recurrent structures. Besides, Seq2Seq-SA-DGP reduces the smoothing problems of recurrent structures and is effective when a simple input for an end-to-end system is given.
The biggest obstacle to develop end-to-end Japanese text-to-speech (TTS) systems is to estimate phonetic and prosodic information (PPI) from Japanese texts. The following are the reasons: (1) the Kanji characters of the Japanese writing system have multiple corresponding pronunciations, (2) there is no separation mark between words, and (3) an accent nucleus must be assigned at appropriate positions. In this paper, we propose to solve the problems by neural machine translation (NMT) on the basis of encoder-decoder models, and compare NMT models of recurrent neural networks and the Transformer architecture. The proposed model handles texts on token (character) basis, although conventional systems handle them on word basis. To ensure the potential of the proposed approach, NMT models are trained using pairs of sentences and their PPIs that are generated by a conventional Japanese TTS system from 5 million sentences. Evaluation experiments were performed using PPIs that are manually annotated for 5,142 sentences. The experimental results showed that the Transformer architecture has the best performance, with 98.0% accuracy for phonetic information estimation and 95.0% accuracy for PPI estimation. Judging from the results, NMT models are promising toward end-to-end Japanese TTS.
Expressive neural text-to-speech (TTS) systems incorporate a style encoder to learn a latent embedding as the style information. However, this embedding process may encode redundant textual information. This phenomenon is called content leakage. Researchers have attempted to resolve this problem by adding an ASR or other auxiliary supervision loss functions. In this study, we propose an unsupervised method called the “information sieve” to reduce the effect of content leakage in prosody transfer. The rationale of this approach is that the style encoder can be forced to focus on style information rather than on textual information contained in the reference speech by a well-designed downsample-upsample filter, i.e., the extracted style embeddings can be downsampled at a certain interval and then upsampled by duplication. Furthermore, we used instance normalization in convolution layers to help the system learn a better latent style space. Objective metrics such as the significantly lower word error rate (WER) demonstrate the effectiveness of this model in mitigating content leakage. Listening tests indicate that the model retains its prosody transferability compared with the baseline models such as the original GST-Tacotron and ASR-guided Tacotron.
Sequence-to-sequence (seq2seq) models have achieved state-of-the-art performance in a wide range of tasks including Neural Machine Translation (NMT) and Text-To-Speech (TTS). These models are usually trained with teacher forcing, where the reference back-history is used to predict the next token. This makes training efficient, but limits performance, because during inference the free-running back-history must be used. To address this problem, deliberation-based multi-pass seq2seq has been used in NMT. Here the output sequence is generated in multiple passes, each one conditioned on the initial input and the free-running output of the previous pass. This paper investigates, and compares, deliberation-based multi-pass seq2seq for TTS and NMT. For NMT the simplest form of multi-pass approaches, where the free-running first-pass output is combined with the initial input, improves performance. However, applying this scheme to TTS is challenging: the multi-pass model tends to converge to the standard single-pass model, ignoring the previous output. To tackle this issue, a guided attention loss is added, enabling the system to make more extensive use of the free-running output. Experimental results confirm the above analysis and demonstrate that the proposed TTS model outperforms a strong baseline.
This paper introduces Parallel Tacotron 2, a non-autoregressive neural text-to-speech model with a fully differentiable duration model which does not require supervised duration signals. The duration model is based on a novel attention mechanism and an iterative reconstruction loss based on Soft Dynamic TimeWarping, this model can learn token-frame alignments as well as token durations automatically. Experimental results show that Parallel Tacotron 2 outperforms baselines in subjective naturalness in several diverse multi speaker evaluations.
Transformer models have shown promising results in neural speech synthesis due to their superior ability to model long-term dependencies compared to recurrent networks. The computation complexity of transformers increases quadratically with sequence length, making it impractical for many real-time applications. To address the complexity issue in speech synthesis domain, this paper proposes an efficient transformer-based acoustic model that is constant-speed regardless of input sequence length, making it ideal for streaming speech synthesis applications. The proposed model uses a transformer network that predicts the prosody features at phone rate and then an Emformer network to predict the frame-rate spectral features in a streaming manner. Both the transformer and Emformer in the proposed architecture use a self-attention mechanism that involves explicit long-term information, thus providing improved speech naturalness for long utterances. In our experiments, we use a WaveRNN neural vocoder that takes in the predicted spectral features and generates the final audio. The overall architecture achieves human-like speech quality both on short and long utterances while maintaining a low latency and low real-time factor. Our mean opinion score (MOS) evaluation shows that for short utterances, the proposed model achieves a MOS of 4.213 compared to ground-truth with MOS of 4.307; and for long utterances, it also produces high-quality speech with a MOS of 4.201 compared to ground-truth with MOS of 4.360.
This paper introduces PnG BERT, a new encoder model for neural TTS. This model is augmented from the original BERT model, by taking both phoneme and grapheme representations of text as input, as well as the word-level alignment between them. It can be pre-trained on a large text corpus in a self-supervised manner, and fine-tuned in a TTS task. Experimental results show that a neural TTS model using a pre-trained PnG BERT as its encoder yields more natural prosody and more accurate pronunciation than a baseline model using only phoneme input with no pre-training. Subjective side-by-side preference evaluations show that raters have no statistically significant preference between the speech synthesized using a PnG BERT and ground truth recordings from professional speakers.
In the model training with neural networks, although the model performance is always the first priority to optimize, training efficiency also plays an important role in model deployment. There are many ways to speed up training with minimal performance loss, such as training with more GPUs, or with mixed precisions, optimizing training parameters, or making features more compact but more representable. Since mini-batch training is now the go-to approach for many machine learning tasks, minimizing the zero-padding to incorporate samples of different lengths into one batch, is an alternative approach to save training time. Here we propose a batching strategy based on semi-sorted samples, with dynamic batch sizes and batch randomization. By replacing the random batching with the proposed batching strategies, it saves more than 40% training time without compromising performance in training seq2seq neural text-to-speech models based on the Tacotron framework. We also compare it with two other batching strategies and show it performs similarly in terms of saving time and maintaining performance, but with a simpler concept and a smoother tuning parameter to balance between zero-padding and randomness level.
Recently, end-to-end Korean singing voice systems have been designed to generate realistic singing voices. However, these systems still suffer from a lack of robustness in terms of pronunciation accuracy. In this paper, we propose N-Singer, a non-autoregressive Korean singing voice system, to synthesize accurate and pronounced Korean singing voices in parallel. N-Singer consists of a Transformer-based mel-generator, a convolutional network-based postnet, and voicing-aware discriminators. It can contribute in the following ways. First, for accurate pronunciation, N-Singer separately models linguistic and pitch information without other acoustic features. Second, to achieve improved mel-spectrograms, N-Singer uses a combination of Transformer-based modules and convolutional network-based modules. Third, in adversarial training, voicing-aware conditional discriminators are used to capture the harmonic features of voiced segments and noise components of unvoiced segments. The experimental results prove that N-Singer can synthesize a natural singing voice in parallel with a more accurate pronunciation than the baseline model.
The idea of using phonological features instead of phonemes as input to sequence-to-sequence TTS has been recently proposed for zero-shot multilingual speech synthesis. This approach is useful for code-switching, as it facilitates the seamless uttering of foreign text embedded in a stream of native text. In our work, we train a language-agnostic multispeaker model conditioned on a set of phonologically derived features common across different languages, with the goal of achieving cross-lingual speaker adaptation. We first experiment with the effect of language phonological similarity on cross-lingual TTS of several source-target language combinations. Subsequently, we fine-tune the model with very limited data of a new speaker’s voice in either a seen or an unseen language, and achieve synthetic speech of equal quality, while preserving the target speaker’s identity. With as few as 32 and 8 utterances of target speaker data, we obtain high speaker similarity scores and naturalness comparable to the corresponding literature. In the extreme case of only 2 available adaptation utterances, we find that our model behaves as a few-shot learner, as the performance is similar in both the seen and unseen adaptation language scenarios.
Cross-lingual text-to-speech (TTS) synthesis on monolingual corpora is still a challenging task, especially when many kinds of languages are involved. In this paper, we improve the cross-lingual TTS model on monolingual corpora with pitch contour information. We propose a method to obtain pitch contour sequences for different languages without manual annotation, and extend the Tacotron-based TTS model with the proposed Pitch Contour Extraction (PCE) module. Our experimental results show that the proposed approach can effectively improve the naturalness and consistency of synthesized mixed-lingual utterances.
Intra-lingual voice conversion has achieved great progress recently in terms of naturalness and similarity. However, in cross-lingual voice conversion, there is still an urgent need to improve the quality of the converted speech, especially with nonparallel training data. Previous works usually use Phonetic Posteriorgrams (PPGs) as the linguistic representations. In the case of cross-lingual voice conversion, the linguistic information is therefore represented as PPGs. It is well-known that PPGs may suffer from word dropping and mispronunciation, especially when the input speech is noisy. In addition, systems using PPGs can only convert the input into a known target language that is seen during training. This paper proposes an any-to-many voice conversion system based on disentangled universal linguistic representations (ULRs), which are extracted from a mix-lingual phoneme recognition system. Two methods are proposed to remove speaker information from ULRs. Experimental results show that the proposed method can effectively improve the converted speech objectively and subjectively. The system can also convert speech utterances naturally even if the language is not seen during training.
In this paper, we present EfficientSing, a Chinese singing voice synthesis (SVS) system based on a non-autoregressive duration-free acoustic model and HiFi-GAN neural vocoder. Different from many existing SVS methods, no auxiliary duration prediction module is needed in this work, since a newly proposed monotonic alignment modeling mechanism is adopted. Moreover, we follow the non-autoregressive architecture of EfficientTTS with some singing-specific adaption, making training and inference fully parallel and efficient. HiFi-GAN vocoder is adopted to improve the voice quality of synthesized songs and inference efficiency. Both objective and subjective experimental results show that the proposed system can produce quite natural and high-fidelity songs and outperform the Tacotron-based baseline in terms of pronunciation, pitch and rhythm.
We present a cross-lingual speaker adaptation method based on domain adaptation and a speaker consistency loss for text-to-speech (TTS) synthesis. Existing monolingual speaker adaptation methods based on direct fine-tuning are not applicable for cross-lingual data. The proposed method first trains a language-independent speaker encoder by speaker verification using domain adaption on multilingual data, including the source and the target languages. Then the proposed method trains a monolingual multi-speaker TTS model on the source language’s data using the speaker embeddings generated by the speaker encoder. To adapt the TTS model of the source language to new speakers the proposed method uses a speaker consistency loss to maximize the cosine similarity between speaker embeddings generated from the natural speech and the same speaker’s synthesized speech. This makes fine-tuning the TTS model of source language on speech data of target language become possible. We conduct experiments on multi-speaker English and Japanese datasets with 207 speakers in total. Results of comprehensive experiments demonstrate that the proposed method can significantly improve speech naturalness compared to the baseline method.
Recently multilingual TTS systems using only monolingual datasets have obtained significant improvement. However, the quality of cross-language speech synthesis is not comparable to the speaker’s own language and often comes with a heavy foreign accent. This paper proposed a multi-speaker multi-style multi-language speech synthesis system (M3), which improves the speech quality by introducing a fine-grained style encoder and overcomes the non-authentic accent problem through cross-speaker style transfer. To avoid leaking timbre information into style encoder, we utilized a speaker conditional variational encoder and conducted adversarial speaker training using the gradient reversal layer. Then, we built a Mixture Density Network (MDN) for mapping text to extracted style vectors for each speaker. At the inference stage, cross-language style transfer could be achieved by assigning any speaker’s style type in the target language. Our system uses existing speaker style and genuinely avoids foreign accents. In the MOS-speech-naturalness, the proposed method generally achieves 4.0 and significantly outperform the baseline system.
Talking head generation is an active research problem. It has been widely studied as a direct speech-to-video or two stage speech-to-landmarks-to-video mapping problem. In this study, our main motivation is to assess individual and joint contributions of the speech and facial landmarks to the talking head generation quality through a state-of-the-art generative adversarial network (GAN) architecture. Incorporating frame and sequence discriminators and a feature matching loss, we investigate performances of speech only, landmark only and joint speech and landmark driven talking head generation on the CREMA-D dataset. Objective evaluations using the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and landmark distance (LMD) indicate that while landmarks bring PSNR and SSIM improvements to the speech driven system, speech brings LMD improvement to the landmark driven system. Furthermore, feature matching is observed to improve the speech driven talking head generation models significantly.
This paper investigates a novel task of talking face video generation solely from speeches. The speech-to-video generation technique can spark interesting applications in entertainment, customer service, and human-computer-interaction industries. Indeed, the timbre, accent and speed in speeches could contain rich information relevant to speakers’ appearance. The challenge mainly lies in disentangling the distinct visual attributes from audio signals. In this article, we propose a light-weight, cross-modal distillation method to extract disentangled emotional and identity information from unlabelled video inputs. The extracted features are then integrated by a generative adversarial network into talking face video clips. With carefully crafted discriminators, the proposed framework achieves realistic generation results. Experiments with observed individuals demonstrated that the proposed framework captures the emotional expressions solely from speeches, and produces spontaneous facial motion in the video output. Compared to the baseline method where speeches are combined with a static image of the speaker, the results of the proposed framework is almost indistinguishable. User studies also show that the proposed method outperforms the existing algorithms in terms of emotion expression in the generated videos.
Several of the latest GAN-based vocoders show remarkable achievements, outperforming autoregressive and flow-based competitors in both qualitative and quantitative measures while synthesizing orders of magnitude faster. In this work, we hypothesize that the common factor underlying their success is the multi-resolution discriminating framework, not the minute details in architecture, loss function, or training strategy. We experimentally test the hypothesis by evaluating six different generators paired with one shared multi-resolution discriminating framework. For all evaluative measures with respect to text-to-speech syntheses and for all perceptual metrics, their performances are not distinguishable from one another, which supports our hypothesis.
Current two-stage TTS framework typically integrates an acoustic model with a vocoder — the acoustic model predicts a low resolution intermediate representation such as Mel-spectrum while the vocoder generates waveform from the intermediate representation. Although the intermediate representation is served as a bridge, there still exists critical mismatch between the acoustic model and the vocoder as they are commonly separately learned and work on different distributions of representation, leading to inevitable artifacts in the synthesized speech. In this work, different from using pre-designed intermediate representation in most previous studies, we propose to use VAE combining with GAN to learn a latent representation directly from speech and then utilize a flow-based acoustic model to model the distribution of the latent representation from text. In this way, the mismatch problem is migrated as the two stages work on the same distribution. Results demonstrate that the flow-based acoustic model can exactly model the distribution of our learned speech representation and the proposed TTS framework, namely Glow-WaveGAN, can produce high fidelity speech outperforming the state-of-the-art GAN-based model.