A computer-implemented method of generating an output example comprising a respective N-bit output value at each generation time step of a sequence of generation time steps, wherein the output value comprises an audio data value for defining an audio waveform that represents an utterance of a piece of text for converting text to speech, the method comprising, for each generation time step: processing a first recurrent input comprising the N-bit output value (112; c t-1 , f t-1 ) at the preceding generation time step in the sequence using a recurrent neural network and in accordance with a hidden state of the recurrent neural network (114; h t-1 ) to generate a first score distribution over possible values for a first half of the N bits in the output value at the generation time step; selecting, using the first score distribution, values for the first half (116; c t ) of the N bits of the output value; processing a second recurrent input comprising (i) the N-bit output value (112; c t-1 , f t-1 ) at the preceding generation time step in the sequence and (ii) the values for the first half of the N bits (116; c t ) using the recurrent neural network and in accordance with the same hidden state (114; h t-1 ) to generate a second score distribution over possible values for a second half of the N bits in the output value at the generation time step; and selecting, using the second score distribution, values for the second half (118; f t ) of the N bits of the output value; wherein the recurrent neural network is conditioned on a sequence of linguistic features of the piece of text; wherein the output value is conditioned on a respective conditioning input at each of the generation time steps, and wherein the first recurrent input and the second recurrent input each also comprise the conditioning input for the generation time step; the method further comprising: generating the conditioning input at the generation time step by processing the linguistic features using a conditioning neural network. processing a first recurrent input comprising the N-bit output value (112; c t-1 , f t-1 ) at the preceding generation time step in the sequence using a recurrent neural network and in accordance with a hidden state of the recurrent neural network (114; h t-1 ) to generate a first score distribution over possible values for a first half of the N bits in the output value at the generation time step; selecting, using the first score distribution, values for the first half (116; c t ) of the N bits of the output value; processing a second recurrent input comprising (i) the N-bit output value (112; c t-1 , f t-1 ) at the preceding generation time step in the sequence and (ii) the values for the first half of the N bits (116; c t ) using the recurrent neural network and in accordance with the same hidden state (114; h t-1 ) to generate a second score distribution over possible values for a second half of the N bits in the output value at the generation time step; and selecting, using the second score distribution, values for the second half (118; f t ) of the N bits of the output value; wherein the recurrent neural network is conditioned on a sequence of linguistic features of the piece of text; wherein the output value is conditioned on a respective conditioning input at each of the generation time steps, and wherein the first recurrent input and the second recurrent input each also comprise the conditioning input for the generation time step; the method further comprising: generating the conditioning input at the generation time step by processing the linguistic features using a conditioning neural network. <EOS>