Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound text comprehension and generation capabilities, have made significant strides across various natural science fields. However, their application in cell biology remains limited and a systematic evaluation of their performance is lacking. To address this gap, in this article, we select seven mainstream LLMs and evaluate their performance across nine gene-related problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, but still lag behind domain-specific models in comprehending transcriptional expression profiles. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs’ ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while being mindful of the associated challenges. We release our code and data at https://github.com/epang-ucas/Evaluate_LLMs_to_Genes .

This paper presents a multilingual speech synthesis approach that leverages learned phonetic representations to eliminate the need for pronunciation dictionaries in target languages. The learned phonetic representations consist of unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR). To extract UPRs, a pre-trained wav2vec 2.0 model is utilized, while a language-independent automatic speech recognition (LI-ASR) model with a connectionist temporal classification (CTC) loss is employed to derive segment-level SPRs from the speech data of target languages. An acoustic model using UPRs and SPRs as intermediate representations is then designed, comprising a UPR predictor, an SPR predictor, and a representation-to-mel-spectrogram (RTM) converter. The two predictors generate UPRs and SPRs from texts, respectively. The RTM converter first combines UPRs with SPRs using a Transformer-based encoder, and then feeds the merged representations into a decoder to produce mel-spectrograms. Considering the difficulty of collecting large training corpora for all languages in multilingual speech synthesis, the parameters of both the two predictors and the RTM converter can be pre-trained on non-target languages to further improve model performance. Experimental results on six target languages demonstrate that our method outperformed the approaches directly predicting mel-spectrograms from character or phoneme sequences, and pre-training the acoustic model using a multilingual corpus further improved the performance of synthetic speech.

This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages.In this method, a pretrained wav2vec 2.0 model is adopted to extract UPRs and a language-independent automatic speech recognition (LI-ASR) model is built with a connectionist temporal classification (CTC) loss to extract segment-level SPRs from the audio data of target languages.Then, an acoustic model is designed, which first predicts UPRs and SPRs from texts separately and then combines the predicted UPRs and SPRs to generate mel-spectrograms.The results of our experiments on six languages show that the proposed method outperformed the methods that directly predicted mel-spectrograms from character or phoneme sequences and the ablated models that utilized only UPRs or SPRs.

With the fast development of Internet of Things (IoT) especially for smart grid and electric vehicle (EV) networking, vehicle-to-grid (V2G) communications have been increasingly studied and recognized as one of the most convincing tools for general road transportation, to effectively reduce the oil demands and gas emissions. Unfortunately, a series of security and privacy issues have significantly impeded its wide adoption. The existing work mainly focused on the static environment, which cannot be directly applied to the mobile setting where EVs travel across regions. The dynamic pricing metric in V2G networks depends on the real-time electricity usage aggregation in one region. To address this issue, in this article, an efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption. In the proposed threshold homomorphic encryption, a legal ciphertext can be generated if and only if no less than threshold k individual illegal ciphertexts are aggregated. Therefore, the aggregated power consumption data can be successfully decrypted while the individual power consumption privacy of honest EV users can be well protected against even the collusion between a malicious power charging station and compromised EVs. Furthermore, the technique of SASD guarantees entity authentication with a minimized amount of transmitted data. Finally, formal security proof and extensive performance evaluation demonstrate the effectiveness and practicability of our proposed PADP.

In this paper, we propose the FeatherWave, yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding. The LPCNet, a recently proposed neural vocoder which utilized the linear predictive characteristic of speech signal in the WaveRNN architecture, can generate high quality speech with a speed faster than real-time on a single CPU core. However, LPCNet is still not efficient enough for online speech generation tasks. To address this issue, we adopt the multi-band linear predictive coding for WaveRNN vocoder. The multi-band method enables the model to generate several speech samples in parallel at one step. Therefore, it can significantly improve the efficiency of speech synthesis. The proposed model with 4 sub-bands needs less than 1.6 GFLOPS for speech generation. In our experiments, it can generate 24 kHz high-fidelity audio 9x faster than real-time on a single CPU, which is much faster than the LPCNet vocoder. Furthermore, our subjective listening test shows that the FeatherWave can generate speech with better quality than LPCNet.

This paper describes the Voice Conversion Challenge 2016 devised by the authors to better understand different voice conversion (VC) techniques by comparing their performance on a common dataset. The task of the challenge was speaker conversion, i.e., to transform the voice identity of a source speaker into that of a target speaker while preserving the linguistic content. Using a common dataset consisting of 162 utterances for training and 54 utterances for evaluation from each of 5 source and 5 target speakers, 17 groups working in VC around the world developed their own VC systems for every combination of the source and target speakers, i.e., 25 systems in total, and generated voice samples converted by the developed systems. These samples were evaluated in terms of target speaker similarity and naturalness by 200 listeners in a controlled environment. This paper summarizes the design of the challenge, its result, and a future plan to share views about unsolved problems and challenges faced by the current VC techniques.

This paper presents a method for voice conversion using deep neural networks (DNNs) trained with multiple source speakers. The proposed DNNs can be used in two ways for different scenarios: 1) in the absence of training data for source speaker, the DNNs can be treated as source-speaker-independent models and perform conversions directly from arbitrary source speakers to certain target speaker; 2) the DNNs can also be used as initial models for further fine-tuning of source-speaker-dependent DNNs when parallel training data for both source and target speakers are available. Experimental results show that, as source-speaker-independent models, the proposed DNNs can achieve comparable performance to conventional source-speaker-dependent models. On the other hand, the proposed method outperforms the conventional initialization method with restricted Boltzmann machines (RBMs).

The generated speech of hidden Markov model (HMM)-based statistical parametric speech synthesis still sounds “muffled.” One cause of this degradation in speech quality may be the loss of fine spectral structures. In this paper, we propose to use a deep generative architecture, a deep neural network (DNN) generatively trained, as a postfilter. The network models the conditional probability of the spectrum of natural speech given that of synthetic speech to compensate for such gap between synthetic and natural speech. The proposed probabilistic postfilter is generatively trained by cascading two restricted Boltzmann machines (RBMs) or deep belief networks (DBNs) with one bidirectional associative memory (BAM). We devised two types of DNN postfilters: one operating in the mel-cepstral domain and the other in the higher dimensional spectral domain. We compare these two new data-driven postfilters with other types of postfilters that are currently used in speech synthesis: a fixed mel-cepstral based postfilter, the global variance based parameter generation, and the modulation spectrum-based enhancement. Subjective evaluations using the synthetic voices of a male and female speaker confirmed that the proposed DNN-based postfilter in the spectral domain significantly improved the segmental quality of synthetic speech compared to that with conventional methods.

The spectral envelope is the most natural representation of speech signal. But in voice conversion, it is difficult to directly model the raw spectral envelope space, which is high dimensional and strongly cross-dimensional correlated, with conventional Gaussian distributions. Bidirectional associative memory (BAM) is a two-layer feedback neural network that can better model the cross-dimensional correlations in high dimensional vectors. In this paper, we propose to reformulate BAMs as Gaussian distributions in order to model the spectral envelope space. The parameters of BAMs are estimated using the contrastive divergence algorithm. The evaluations on likelihood show that BAMs have better modeling ability than Gaussians with diagonal covariance. And the subjective tests on voice conversion indicate that the performance of the proposed method is significantly improved comparing with the conventional GMM based method.

In this paper we propose a deep neural network to model the conditional probability of the spectral differences between nat-ural and synthetic speech. This allows us to reconstruct the spectral fine structures in speech generated by HMMs. We com-pared the new stochastic data-driven postfilter with global vari-ance based parameter generation and modulation spectrum en-hancement. Our results confirm that the proposed method sig-nificantly improves the segmental quality of synthetic speech compared to the conventional methods. Index Terms: HMM, speech synthesis, DNN, modulation spectrum, postfilter, segmental quality

This paper presents a new spectral envelope conversion method using deep neural networks (DNNs). The conventional joint density Gaussian mixture model (JDGMM) based spectral conversion methods perform stably and effectively. However, the speech generated by these methods suffer severe quality degradation due to the following two factors: 1) inadequacy of JDGMM in modeling the distribution of spectral features as well as the non-linear mapping relationship between the source and target speakers, 2) spectral detail loss caused by the use of high-level spectral features such as mel-cepstra. Previously, we have proposed to use the mixture of restricted Boltzmann machines (MoRBM) and the mixture of Gaussian bidirectional associative memories (MoGBAM) to cope with these problems. In this paper, we propose to use a DNN to construct a global non-linear mapping relationship between the spectral envelopes of two speakers. The proposed DNN is generatively trained by cascading two RBMs, which model the distributions of spectral envelopes of source and target speakers respectively, using a Bernoulli BAM (BBAM). Therefore, the proposed training method takes the advantage of the strong modeling ability of RBMs in modeling the distribution of spectral envelopes and the superiority of BAMs in deriving the conditional distributions for conversion. Careful comparisons and analysis among the proposed method and some conventional methods are presented in this paper. The subjective results show that the proposed method can significantly improve the performance in terms of both similarity and naturalness compared to conventional methods.

This paper presents a non-parallel training algorithm for voice con version based on feature transform Gaussian mixture model (FT GMM), which is a mixture model of joint density space of source speaker and target speaker with explicit feature transform modeling. In FT-GMM, the correlations between the distributions of two speakers in each component of the mixture model are not directly modeled, but absorbed into these explicit feature transformations. This makes it possible to extend this model to non-parallel training by simply decomposing it into two sub-models, one for each speaker and optimizing them separatively. A frequency warping process is adopted to compensate performance degradation caused by original spectral distance between source and target speakers. Cross-gender experimental results show that the proposed method achieves comparable performance as parallel training.